i arthshodh - university of rajasthan
TRANSCRIPT
i
ISSN 2230–7877
Arthshodh A Peer-reviewed bi-annual Journal of Department of Economics,
University of Rajasthan, Jaipur, India
Vol. VI No. 03 January–June , 2020
CONTENTS
ARTICLES Page No.
1. A Comparison of Determinants of Infant Mortality Rate between Desert and Non-Desert Districts of Rajasthan
Dr. M. R. Singariya
01-14
2. Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide Lockdown on Indian Economy
Dr. Rami Gaurang Dattubhai
15-36
3. Cost Benefit Analysis of Tomato Cultivation Under
Polyhouse in Haryana
Komal Malik & Vinay Kumar
37-44
4. Growth Analysis of Area, Production and Yield of Rapeseed and Mustard Crop in Rajasthan
Preeti Prasad, Rashmi Bhargava & S.K Kulshrestha
45-52
5. Inequality Re-examined Amidst Covid-19
Dr. G. L. Meena
53-70
6. Climate Change and Its Impact on Agricultural Production: An Evidence from India
Dr. Chitra Choudhary & Sumedha Bhatnagar
71-91
Accounting Studies i Volume 11 No. 1 May, 2013
Chief Editor’s Voice
A Forgettable 2020…
India’s rapid economic growth in recent decades has lifted the
country to become the world’s third-largest economy (in purchasing
power parity terms), while major economic reforms have helped
dramatically reduce poverty since 2002. As 2020 draws to a close, here
is a recap of how the Indian economy fared in a year upended by the
corona virus pandemic. From contracting by an unprecedented 23.9
per cent to plunging into a technical recession, the trajectory of India’s
economy saw a steep decline in 2020-primarily due to the Covid-19
pandemic. After reporting its first case in late January 2020 in the
southern state of Kerala, India introduced rigorous airport screenings
for the corona virus (COVID-19). The following weeks saw a quick
succession of events leading to a suspension of all travel in and out of
the country and announced country-wide lockdown by March 22.The
outbreak of the Covid-19 pandemic is an unprecedented shock to the
Indian economy. The economy was already in a parlous state before
Covid-19 struck. With the prolonged country-wide lockdown, global
economic downturn and associated disruption of demand and supply
chains, the economy is likely to face a protracted period of slowdown.
The magnitude of the economic impact will depend upon the
duration and severity of the health crisis, the duration of the
lockdown and the manner in which the situation unfolds once the
lockdown is lifted. While some of the effects of Covid crisis on the
economy are short term, many can have lasting impacts. The
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lockdowns have hugely impacted the supply-chain management and
sent the GDP and import export cycle plummeting. There are three
major areas of impact for Indian businesses which are linkages,
supply chain and macroeconomic factors. This is indeed the worst
recession since the Great Depression in the 1930s.
How the Government Responded to the biggest Crisis
All anecdotal evidence available, such as hundreds of
thousands of stranded migrant workers across the country,
suggested that the Medium, Small and Micro Enterprises (MSMEs)
were the worst casualty of Covid-19 induced lockdown. Hence, the
government laid its primary focus to lift the stressed MSME sector
with its relief packages, especially a massive increase in credit
guarantees to them. It essentially means that the government has
resorted to taking over the credit risk of MSMEs should they want to
remain in business. A credit guarantee by the government helps as it
assures the bank that its loan will be repaid by the government in
case the MSME falters.
Reserve Bank of India (RBI) has taken some necessary steps to
meet the crisis situation in the country. RBI came up with Business
Continuity Plan in the emerging situation and is sharing instructions,
by devising strategies between the staff member and other customers.
RBI has also started Open Market Operations from March 20 in the
form of purchase of an aggregate amount of Rs 10,000 crores of
government securities. There are no notified securities amount
mentioned, but RBI has a self-imposed ceiling of Rs 10,000 crore
wherein they have the sole right to decide the purchase of individual
securities, accept offer either less or higher than the amount of Rs
10,000 crores and accepting or rejecting the offer.
On May 12, the Prime Minister, announced a special
economic package of Rs 20 lakh crore (equivalent to 10% of India’s
GDP) with the aim of making the country independent against the
tough competition in the global supply chain and to help in
empowering the poor, labourers, migrants who have been adversely
Chief Editor iii
affected by COVID. The Atmanirbhar Bharat (Self-reliant India)
package, rolled out in several tranches to mitigate the biggest crisis
since 1979, reinforced the ‘fiscal conservatism’ ideology of the
government under Prime Minister–rather than large cash transfers,
the growth philosophy centres around creating an ecosystem that
aids domestic demand, incentivizes companies to generate jobs and
boost production, and simultaneously extends benefits to those in
severe distress.
India needs to continue implementing critical reforms in key
areas like health, labour, land, skills and finance to come out
stronger from the impact of Covid-19 pandemic, the World Bank
said these reforms should aim at enhancing productivity of the
Indian economy and spur private investments and exports. It also
provides a more in-depth analysis of selected economic and policy
issues and highlights the economic reforms that India has been
undertaking and needs to continue with in the medium to long-
term. Investing in infrastructure, labour, land and human capital
will give India the ability to navigate the uncertainties and be more
competitive as the world emerges from the pandemic. To put the
financial sector on a sounder footing, financial sector stability,
reforms in the non-banking finance company (NBFC) sector, deeper
capital market reforms, mainstreaming fintech to reach firms faster
and at a lower cost, and moving to a more strategic public-sector
footprint. The recent liquidity and performance issues in the
financial sector, exacerbated by the Covid-19 crisis, present
policymakers with a strong reason–and an opportunity–to accelerate
efforts towards building a more efficient, stable and market-oriented
financial system. It is encouraging that the government is moving to
a more selective and strategic public sector footprint in the financial
sector. International experience shows this can boost the banking
sector's ability to support credit, facilitate effective financial
intermediation and reduce fiscal exposure. The current crisis has
also brought to the forefront new economic opportunities in the
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areas of digital technology, retail, health-technology and education-
technology services besides global demand in areas such as
pharmaceuticals, medical equipment, and protective gear. These
opportunities can provide new growth levers for India.
S. S. Somra
Accounting Studies 1 Volume 11 No. 1 May, 2013
A Comparison of Determinants of Infant Mortality Rate between Desert and Non-Desert
Districts of Rajasthan
Dr. M. R. Singariya
Abstract
Pooled OLS and fixed effects panel regression models were used to
examine the determinants of infant mortality rate in Rajasthan using
district level data separately for twelve desert and twenty non desert
districts for the period of 1991 and 2001. Data were obtained from Human
Development Report Rajasthan, (An update 2008) published by Institute of
Development Studies Jaipur. In our empirical work, the explanatory
variables used are NSDP per capita, % share of urban population, % share
of primary sector employment, female literacy rate, female wok participation
rate, crude birth rate, total fertility rate and household access facilities like
electricity, safe drinking water and toilet.
The analysis seeks to determine which of socioeconomic variables
play an important role in reducing infant mortality rates. The results of
pooled data showed that the Crude birth rate has significant at 10% level
negative association in desert districts, while this association is reversed
and yet significant at 5% level was witnessed in non desert districts of
Rajasthan. This result increases our interest to investigate the present
application in depth. Fixed effects panel regression showed that IMR has
significant and negative association with CBR, household access electricity
and toilet facilities and positive association with female work participation
Associate Professor, Department of Economics, S D Government College
Beawar, Ajmer, Rajasthan, (Rajasthan).
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rate and share of urban population in the desert districts of Rajasthan.
While non desert districts analysis showed that higher female work
participation reduces the extent of IMR and this effect is statistically
significant (at 5%). This result is in accordance to earlier studies. Apart
from female work participation in reducing IMRs in non desert districts of
Rajasthan, household’s access toilet facility also seem to have significant
and negative influence on IMR. Higher levels of CBR, SPSE and SHOUP
are associated with higher levels of IMR in all 32 districts of Rajasthan. The
Double log Panel regression suggest that one percent increase in CBR and
SHOUP, on average increases IMR by 1.78% and 1.33% respectively in all
the districts of Rajasthan. So the policy implications of the findings are
clear and suggesting to control on CBR and urbanization or strengthening
health care facilities in urban areas of Rajasthan for reduction of IMR as
well as to capture Millennium Development Goals.
Key words: Infant Mortality Rate, Fixed effects panel, Desert, Rajasthan
Introduction
Rajasthan is situated in the northern part of India. It is the
largest state in India by area constituting 10.4 percent of the total
geographical area of India and it accounts for 5.5 percent of population
of India (Census of India, 2001). It is administratively divided in to 7
divisions and 32 districts. Recently, a new district has been carved out
in the state namely, Pratapgarh. Currently, there are 33 districts.
Topographically, deserts in the state constitute a large chunk of the
land mass, where the settlements are scattered and the density of
population is quite low.
There have been common and comparable state efforts of
development among all its districts, but different topography and
cultural practices have resulted in differences among desert and non-
desert districts in the state with respect to certain parameters. Thus it
was expected that there would be different influencing factors affecting
Infant Mortality Rate (IMR) in the desert and non-desert districts.
Though, the State has shown some progress on the human
development front with the human development index showing
A Comparison of Determinants of Infant Mortality Rate between Desert ....... 3
progress from a rank of 12 in 1981 to 9 in 2001 amongst the 15 major
States (National Human Development Report 2001), the Millennium
Development Goals (MDGs) of health are far behind the desired levels.
The Infant Mortality Rate (IMR), which is considered to be one of the
most sensitive indicators of human development lies at 63 infant deaths
per 1,000 live births compared to 53 per 1,000 live births in case of India
(SRS, 2008) Infant Mortality Rate (IMR) in the state has maintained near
stagnancy for most of the nineties. However, in the new millennium
decline in IMR was sharper. The aggregate IMR declined from 85 in
1995 to 80 in 2001 and further to 67 in 2004 (SRS Bulletin, April 2006).
In this study an attempt has been made to examine the
predictors of infant mortality rate in desert and non-desert districts of
Rajasthan. The specific objectives of this study are; to identify the
factors which are affecting infant mortality and to suggest viable
strategies to reduce the level of infant mortality in the state, so that an
effective measure can be used to meat out the stagnancy of IMR in the
state.
Method
This study looked at the following district level factors;
percentage of households access electricity, safe drinking water, toilet
facility, female literacy, female work participation, share of urban
population, share of primary sector employment, per capita net state
domestic product, crude birth rate, total fertility rate. The study assed
ten factors in all to describe socioeconomic characteristics and its
relationship with Infant mortality rate at district level. Furthermore we
run regressions separately for desert and non-desert districts to identify
those variables, out of the variables considered, which specifically
influence the response variables, the IMR in the desert and non desert
part of the state. The desert districts included in this study are Barmer,
Bikaner, Churu, Ganganagar, Hanumangarh, Jaisalmer, Jalore,
Jhunjhunu, Jodhpur, Nagaur, Pali and Sikar, mainly situated in the
northern-western part of the state. The non-desert districts which are
situated in southern-eastern part of the state and included in this study
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are Ajmer, Alwar, Banswara, Baran, Bharatpur, Bhilwara, Bundi,
Chittorgarh, Dausa, Dholpur, Dungarpur, Jaipur, Jhalawar, Karauli,
Kota, Rajsamand, Swai Madhopur, Sirohi, Tonk and Udaipur.
The data needed for the study have been collected from
Human Development Report Rajasthan, (an update 2008) published
by Institue of Development Studies Jaipur and Rajasthan Health
Scenario 2000, published by Indian Institute of Health Management
Research Jaipur. Pooled OLS and fixed effects panel regression
models have been employed for analysis in this paper. The infant
mortality rate has been used as dependent variable and ten
socioeconomic variables have been used as explanatory variables.
Results and Discussion
Table 1 gives the mean and standard deviation (SD) of the
eleven variables considered to describe socioeconomic status and
their links with IMR in desert and non desert districts of Rajasthan.
It is observed from the table 1, that there have been common and
comparable state efforts of development in the desert and non-desert
parts of the state, as households access electricity facility, share of
primary sector employment and female literacy rate matched well.
Table 1: Mean and SD of the considered variables in desert and
non-desert districts of the state
S. N.
Name of Variables
Description Non Desert Districts (20)
Desert Districts (12)
All Districts (32)
Mean S.D. Mean S.D. Mean S.D.
1 HAE % of H/H access electricity
42.81 15.22 42.8 15.59 42.81 15.24
2 HASWD % of H/H access safe drinking water
78.97 22.34 69.13 14.8 75.28 20.29
3 HATF % of H/H access toilet facility
18.68 9.67 29.25 20.84 22.64 15.59
4 CBR Crude Birth Rate 29.78 7.05 29.24 5.84 29.58 6.58
5 TFR Total Fertility Rate 4.02 1.14 3.97 1.12 4 1.12
6 FWPR Female Work Participation Rate
31.11 9.61 29.02 6.99 30.33 8.72
7 SPSE % share of Primary sector employment
69.33 11.92 69.69 8.38 69.46 10.66
8 SHOUP % share of urban
population
20.2 12.07 21.28 8.98 20.61 10.95
A Comparison of Determinants of Infant Mortality Rate between Desert ....... 5
9 NSDP/c Per capita Net state
domestic product 8397 4494 8037 4525 8262 4473
10 FLR Female literacy rate 31.04 12.86 31.68 16.06 31.28 14.02
11 IMR Infant Mortality Rate 81.16 19.95 67.58 16.76 76.07 19.82
It is also observed from this table that there has been good
socioeconomic development in the desert; however provisions for
safe drinking water and per capita net state domestic product are
still lower in comparison to non-desert districts of the state. But
more interesting fact is that lower level of infant mortality is
witnessed with higher % of urban population and toilet facilities in
the desert part of the state. It is observed that state average statistics
is match well with non-desert districts.
Next, we examine the relationship between infant mortality
rate and female literacy rate pooling the data for the two periods,
1991 and 2001. The scatter plot with a trend line is exhibited in figure
1 for desert districts and figure 3 for non-desert districts separately.
It is amply evident that there is a negative association between IMR
and female literacy. The decline in IMR as female literacy increases is
higher in desert districts in comparison to non-desert districts. The
association between per capita NSDP and IMR in desert districts is
shown in figure 2 and in non desert districts is shown in figure 4.
The decline in IMR as per capita NSDP increases is not uniform in
desert and non-desert districts. The decline is higher in non-desert
districts.
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Our discussions on the correlations that exist between IMR
and female literacy or per capita NSDP-cannot be interpreted as a
cause-effect relationship, including also the possibility of two-way
causality among the above mentioned variables. We now develop an
econometric framework to examine the causal or simultaneity
among these variables.
Tablet 2: Results from Pooled OLS Estimation of IMR
S.N. Name of Variables
Desert Districts (12) Non Desert Districts (20) All Districts (32)
Coefficient t ratio Coefficient t ratio Coefficient t ratio
1 HAE 0.443 0.9176 -0.1263 -0.2532 -0.158 -0.4452
2 HASWD -0.007 -0.0145 -0.0279 -0.090 -0.018 -0.073
3 HATF -0.165 -0.435 -1.7836 -2.46** -0.526 -2.2**
4 CBR -4.379 -2.09 * 4.2932 2.221** 2.173 1.495
5 TFR 37.014 2.6** -26.0014 -2.044* -8.137 -0.77
6 FWPR 0.147 0.1287 -0.6220 -1.293 -0.219 -0.497
7 SPSE 1.046 1.16 -0.6101 -1.291 0.033 0.091
8 SHOUP 0.286 0.5902 0.9246 2.224** 0.478 1.502
9 NSDP/c 0.003 1.422 0.0003 0.1654 0.001 1.038
10 FLR -0.488 -1.047 -0.5213 -1.128 -0.403 -1.264
11 Constant -57.326 -0.556 155.517 2.191** 61.529 1.072
12 Observations 24.000 40.0000 64.000
13 R-Squared 0.685 0.5415 0.398
Note: - * significant at 1o% level, ** significant at 5% level and *** significant at 1%
level
A Comparison of Determinants of Infant Mortality Rate between Desert ....... 7
Table 2 shows the results of pooled OLS regression of IMR on
set of explanatory variables for desert, non-desert and all 32 districts.
R2 % in the desert is 68.54 and in non-desert is 54.15, performance in
OLS regression model justifies the variable selection for to find out
determinants of IMR in desert and non-desert districts. The results of
pooled OLS regression shows that IMR has significant and negative
association with CBR and positive association with TFR in the desert
districts, where as this association is reversed and yet significant in
non desert districts of Rajasthan. It is also witnessed that IMR has
significant and negative association with % H/H access toilet
facilities and positive association with % urban population in non
desert districts.
Table 3: Fixed Effects Panel Data Regression Results of IMR
S.
N.
Name of
Variables Desert Districts (12) Non Desert Districts (20) All Districts (32)
Coefficient t ratio Coefficient t ratio Coefficient t ratio
1 HAE -25.525 -9.48* 0.846 0.9493 -0.471 -0.93
2 HASWD 25.687 9.392* 0.180 0.5815 0.097 0.4
3 HATF -16.086 -9.21* -2.596 -1.959* 0.791 1.83*
4 CBR -34.979 -9.51* 9.628 2.780** 4.576 2.449**
5 TFR 422.111 9.72* -91.759 -2.313** -24.910 -1.853*
6 FWPR 15.143 9.405* -1.275 -2.931** -0.864 -2.24**
7 SPSE 0.289 1.064 0.291 0.3484 1.105 2.034*
8 SHOUP 110.671 9.424* 0.332 0.06378 4.867 2.112**
9 NSDP/c 0.144 9.69 * 0.004 1.168 0.001 0.493
10 FLR 0.210 1.21 0.007 0.007422 -0.302 -0.526
11 Time Dummy -705.940 -9.78 * -114.970 -1.951* -5.600 -0.2076
12 Constant -4422.760 -9.66* 201.623 1.082 -111.790 -1.42
13 Observations 24 40 64
14 R-Squared 0.999 0.934 0.898
Note: * significant at 1o% level, ** significant at 5% level and *** significant at 1%
level
Because the OLS estimates do not control for unobserved
heterogeneity, they cannot be interpreted as casual effects. These
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results only tell us the signs and statistical significance of different
coefficients. Now we present estimated results for the fixed effects
model in table 3. The R-Squared figure has now gone up to .99 for
desert districts, .93 for non-desert districts and .897 for all districts.
Here also we see that our variables are of expected signs. The
coefficients of HAE & HATF for desert districts and HATF, CBR,
TFR and FWPR for non-desert districts are of expected and
significant sign in the fixed effects specification. The coefficient on
time trend is negative and statistically significant for both desert and
non-desert districts. We could interpret this result as the fact that
over the time there is an improvement in general awareness and
aspirations of the people, which are not captured from our
institutional and policy variables. So irrespective of the other factors,
there are some fundamental changes in the society over the decade
that is actually favourable to better outcome in reducing IMR.
Table 4: Fixed Effects Panel Data Regression Results of Log IMR
S.N. Name of
Variables
Desert Districts (12) Non Desert Districts (20) All Districts (32)
Coefficient t ratio Coefficient t ratio Coefficient t ratio
1 Log HAE -1.842 -13.2** 0.087 0.2739 -0.539 -1.533
2 Log HASWD -1.391 -6.996 * 0.384 2.574** 0.123 0.732
3 Log HATF 0.669 5.622 -0.667 -2.751** 0.027 0.1483
4 Log CBR -2.088 -8.477* 3.650 4.207*** 1.775 2.459**
5 Log TFR 0.721 3.715 -3.756 -4.023*** -1.279 -1.994*
6 Log FWPR -0.521 -3.085 -0.194 -2.776** -0.155 -1.982*
7 Log SPSE 0.698 3.334 -0.236 -0.4299 0.055 0.1251
8 Log SHOUP 0.952 3.772 0.754 1.11 1.325 2.166**
9 Log NSDP/c 0.726 8.777 * 0.188 0.4454 0.136 0.4488
10 Log FLR 0.630 8.527* -0.065 -0.1755 -0.018 -0.09
11 Time Dummy -0.837 -7.933 * -0.840 -1.269 -0.100 -0.238
12 Constant 8.656 4.306 -4.487 -0.8666 -3.220 -0.715
13 Observations 24 40 64
14 R-Squared 0.999 0.962 0.896
Note: * significant at 1o% level, ** significant at 5% level and *** significant at 1%
level
A Comparison of Determinants of Infant Mortality Rate between Desert ....... 9
Finally, we present the fixed effects panel regression results
for double log specification with respect to IMR in desert and non-
desert districts of Rajasthan, the following observations are
particularly noteworthy.
(1) Higher the level of electricity and safe drinking water
facilities in desert districts of Rajasthan reduces the extent of
IMR and this effect is statistically significant. This result is in
accordance to earlier studies. A 1% increase in the percentage
of household’s access electricity facility in the desert districts
is associated with 1.84% decrease in IMRs. This result is
statistical significant at 5% level. While on average a 10%
increase in percentage of household’s access safe drinking
water facility is required to decrease the IMRs by 14 per
thousand in the twelve desert districts of Rajasthan. This
result is also significant at 10% level.
(2) Female literacy and per capita net state domestic product have a
positive and statistical significant at 10% level association with
IMR in desert districts. This unfortunate and unmatched result
is an outcome of the lower level of female literacy (particularly
in 1991) and Per capita NSDP existed in desert zone of
Rajasthan. Because of all high Per capita income generating
districts including Ganganagar, Hanumangarh, Kota, Ajmer,
Bhilwara and Rajsamand have witnessed high IMR as per HDI
Report of Rajasthan, an update 2008.
(3) Percentage of household’s access toilet facilities at home has
negative and statistically significant at 5% level influence on
IMR in non-desert twenty districts of Rajasthan. A 1% increase
in HATF is associated with a 0.66% drop in IMRs on average.
(4) Higher the levels of crude birth rates are associated with
higher levels of IMRs in non-desert districts of Rajasthan. A
1% increase in CBR leads to an increase in IMR by 3.65% on
average in non-desert districts of Rajasthan.
10 Arthshodh
(5) Higher female work participation reduces the extent of IMRs
in non-desert districts of Rajasthan and this result is
statistically significant at 5% level. This result is in accordance
to earlier studies. A mother who works outside the house has
greater resources, better access and information to health care
facilities and hence can take better care of child, implying
lower infant mortality rates in the household. A 1% increase
in female work participation rate is associated with 0.19%
decrease in IMRs in non desert twenty districts of Rajasthan,
mainly situated in southern-eastern part of the state.
(6) Though, there is negative influence of female literacy and
percentage share of primary sector employment was observed
with IMRs in non-desert districts, but it was not statistically
significant.
(7) Higher proportion of urban population in the total population
increases the extent of infant mortality in all the districts of
Rajasthan. The effect of urbanization (% urban population) on
IMR is positive and significant. There could be various other
factors that might influence IMR in urban areas like greater
level of pollution, over burden of population might widening
the gape between availability of health care facilities and its
requirements. On average 1% increase in SHOUP increases
IMRs by 1.33 % in all the districts of Rajasthan.
(8) Crude birth rate has highest and statistically significant
positive influence on IMRs in all the districts of Rajasthan. A
1% increase in CBR increases IMRs by 1.78% on average in all
the districts of Rajasthan.
(9) Female work participation rate has significant and negative
association with IMRs in southern-eastern districts of Rajasthan.
On average 6% increase in Female work participation rate is
required to reduce 10 infant deaths per thousand.
A Comparison of Determinants of Infant Mortality Rate between Desert ....... 11
(10) Apart from female work participation rate female literacy also
seem to have negative influence on infant mortality but it is
not statistically significant in this equation.
Conclusion and Policy Suggestions
This study examines the determinants of infant mortality using
panel data of twelve desert and twenty non-desert districts of
Rajasthan for the period 1991-2001. In particular we have focused on
female literacy, female work participation rate, per capita NSDP,
demographic variables (CBR & TFR) and household access facility
variables (HAE, HASDW & HATF) which can be changed using policy.
We estimated the impact of these variables on IMRs using OLS, fixed
effects panel regression and its double log specification. The following
important findings emerge from our econometric analysis:
The descriptive analysis indicates that desert districts have
experienced lower level of IMR in spite of lower level of per
capita income, female work participation rate and percentage of
household access safe drinking water facilities.
The estimated results from fixed effects panel regression
showed that IMR has significant and negative association with
CBR, household access electricity and toilet facilities and
positive association with female work participation rate and
share of urban population in the desert districts of Rajasthan.
While non desert districts analysis showed that higher female
work participation reduces the extent of IMR and this effect is
statistically significant (at 5%). This result is in accordance to
earlier studies. Apart from female work participation in
reducing IMRs in non desert districts of Rajasthan, household’s
access toilet facility also seem to have significant and negative
influence on IMR.
Higher the level of electricity and safe drinking water facilities
in desert districts of Rajasthan reduces the extent of IMR and
this effect is statistically significant. This result is in accordance
12 Arthshodh
to earlier studies. A1% increase in the percentage of household’s
access electricity facility in the desert districts is associated with
1.84% decrease in IMRs. This result is statistical significant at 5%
level. While on average a 10% increase in percentage of
household’s access safe drinking water facility is required to
decrease the IMRs by 14 per thousand in the twelve desert
districts of Rajasthan. This result is also significant at 10% level.
Percentage of household’s access toilet facilities at home has
negative and statistically significant at 5% level influence on
IMR in non-desert twenty districts of Rajasthan. A 1% increase
in HATF is associated with a 0.66% drop in IMRs on average.
The findings of this study clearly demonstrate the role of
household facilities in reducing IMR is very high, in fact, much
higher than per capita NSDP and female literacy. Any increase
in electricity and safe drinking water facility in desert districts
and any increase in toilet facilities in non-desert districts would
have considerably high negative influence on IMR. Increasing
investment in household facilities is a required policy
intervention for reducing IMR. Economic variables like female
literacy and female work participation rate were found to be
negatively associated with IMRs. Policies promoting female
education and participation would also have the desirable effect
of reducing IMR. Higher the level of CBR and SHOUP show
high level of infant mortality suggesting to control on CBR and
urbanization or strengthening health care facilities in urban
areas of Rajasthan for reduction of IMR as well as to capture
Millennium Development Goals.
References
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no. 19 May 8, 2010 P.P. 72
2 Dixit,A.K., Anand,P.K. & Sharma,RC. (2006), A study of district
level development factors influencing infant mortality rate and life
A Comparison of Determinants of Infant Mortality Rate between Desert ....... 13
expectancy in Indian thar desert’, Journal of Rural and Tropical
Public Health 5: 42-45,
3 Govt. Raj 2000Rajasthan 2001: population policy, department of
Health and family Welfare Government of Rajasthan, Jaipur
4 Govt. India (2006) “ Rajasthan Development report” Planning
commission, New Delhi, P.P. 267
5 Gulati, S.C. 1992 ‘Development Determinants of Demographic
variable in India: A district level Analysis’ in Journal of
Quantitative Economics Vol. 8 No. 1 (January 1992), P.P. 157&172
Delhi School of Economics, Delhi
6 Gulati, S.C. and Suresh Sharma ‘( ) Fertility and RCH status in
Uttaranchal and Uttar pradesh. A district level Analysis, Institute
of Economics Growth
7 Hobcraft, JN and McDonald, JW and Rustein, SO (1985),
Demographicc determinanats of infant and early child mortality: A
comparative Analysis’, Population Studies 39(3), 363{385.
8 Kameswararao Avasarala (Jan 2009) : Quality of life Assessment at
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of Karim Nagar” publication by India Journal of community
medicine/Vol 34/issue-1/Janury 2009, P.P. 24.
9 Messias E. (1985), Income inequality, Illiteracy rate and life
expectancy in Brazil. American Journal of Public Health 93: 1294-6.
10 Nag, M. (1983), Impact of social and economic development on
mortality: Comparative study of Kerala and West Bengal’,
Economic and Political Weekly 18(19), 877{900.
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4,6
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management research, Jaipur. WHO collaborating centre for
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14 Arthshodh
14 Report (2001), various issues from directorate of Economics &
Statistics department of Government of Rajasthan, Jaipur.
15 Sen, A. (1987), Mortality as as an indicator of economic success and
failure’, The Economic Journal 108(446), 1{25.
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Accounting Studies 15 Volume 11 No. 1 May, 2013
Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide Lockdown on Indian Economy
Dr. Rami Gaurang Dattubhai1
Abstract
So far crores of people are infected and lakhs of people have died due
to spread of Coronavirus (COVID-19) in India. India hadwitnessed a rapid
spread of the infection due to Coronavirus, leading to the Government
putting the whole country on 21 day nationwide lockdown. Apart from this
Government of India declares COVID-19 a 'National Disaster',
establishing COVID-19 Economic Response Task Force, announcement of
‘Janata Curfew’, Relief package to help fight the COVID-19 outbreak and
measures taken by Reserve Bank of India, Emergency Response and Health
System Preparedness Package, etc. Due to the mass exodus of migrant
workers, vulnerability in rural areas and huge existence of unorganized
sector; Government is facing difficulties in implementing lockdown in
India. Government is facing several challenges viz. limited testing, lack of
strong and well equipped public healthcare, amount of elderly people and
high population density, less number of Personal Protective Equipment
(PPE) and ventilators while combating with COVID-19. Various national
and international rating agencies have significantly reduced growth rates
projections for the financial year 2020-21. As per FICCI survey, tourism,
hospitality and aviation are among the worst affected sectors that are facing
the maximum brunt of the present Coronavirus pandemic. Consumption is
Professor, Department of Economics, Veer Narmad South Gujarat University,
Surat, Gujarat, India. 1 Views expressed are personal and compiled from various sources.
16 Arthshodh
also getting impacted due to job losses and decline in income levels of
people, particularly the daily wage earners due to slowing activity in
several sectors. The coronavirus lockdown will have an adverse effect on the
MSMEs and agriculture sector in India. Due to lockdown it expected that
poverty, unemployment and inflation will increase in India. Government,
economic policy makers and planners have to formulate appropriate
economic policies and strategies once when the lockdown has lifted to
sustain and increase growth rates of different sectors of an economy
without compromising with the social welfare of different segments and
sections of society in days to come.
Keywords: Coronavirus, COVID-19, Rating Agencies, Industries and
Sectors, Indian Economy.
Introduction
The first three cases of Coronavirus pandemic in India were
reported on 30 January, 2020 in Kerala, all of whom were students
who had returned from Wuhan, China. So far over crores of people
have infected and around lakhs of people have died due to
pandemic spread of Coronavirus in India. There are huge variations
in Coronavirus infected persons across the various states and union
territories in India. Experts suggest the number of infections could
be a substantial underestimate, as India's testing rate is one of the
lowest in the world. The outbreak has been declared an epidemic in
various states and union territories, where provisions of
the Epidemic Diseases Act, 1897 have been invoked, and educational
institutions and many commercial establishments have been shut
down. India has suspended all tourist visas, as a majority of the
confirmed cases were linked to other countries. On 22 March 2020,
India observed a 14-hour voluntary public curfew at the insistence of
the Prime Minister Narendra Modi. Further, on 24 March, the prime
minister ordered a nationwide lockdown for 21 days, affecting the
entire 1.3 billion population of India. On 14 April, Prime
Minister Narendra Modi extended the nationwide lockdown until 3
May, with a conditional relaxation after 20 April for the regions
Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide ...... 17
where the spread has been contained. On 1 May, the Government of
India extended nationwide lockdown further by two weeks until 17
May. The Government divided the entire nation into three zones –
green zone, red zone and orange zone. On 17 May, nationwide
lockdown was further extended till 31 May by National Disaster
Management Authority.The transmission escalated in the month of
March, after several cases were reported all over the country, most
of which were linked to people with a travel history to affected
countries.
Measures taken by Government of India to reduce the spreading
of COVID-19
1 Declares COVID-19 A 'National Disaster': Amid the
coronavirus outbreak, the central government on March 14, 2020
declared COVID-19 as a national 'disaster' and announced to
provide ex-gratia relief of Rs 4 lakh to the families who died of
the virus. The move by the Centre would allow the states to
spend larger chunk of funds from the State Disaster Response
Fund (SDRF) to fight the pandemic (ABP News Bureau, 2020).
2 COVID-19 Economic Response Task Force:To deal with the
economic challenges caused by the pandemic, Prime
Minister Narendra Modi on March 19, 2020 announced the
creation of ‘COVID-19 Economic Response Task Force’ under
the Union Finance Minister Nirmala Sitharaman. The Task
Force will consult stakeholders, take feedback, on the basis of
which decisions will be taken to meet the challenges. The Task
Force will also ensure implementation of the decisions taken to
meet these challenges (Modi, N., 2020).
3 ‘Janata Curfew’:The Prime Minister Narendra Modi, in a
televised address to the nation (March 19, 2020), announced a
‘Janata Curfew’ from 7 am to 9 pm Sunday, March 22, 2020 to
stop the spread of coronavirus and pushes social distancing.
This nationwide voluntarily 14 hours self-quarantine exercise
18 Arthshodh
led to a complete lockdown in various states with some even
resorting to Section 144 (Chandra, H. and Basu, M., 2020).
4 Nationwide lockdown for 21 days: Prior to this announcement,
numerous containment measures had already been imposed,
varying in intensity across the country, including travel
restrictions (complete restriction of incoming international
commercial passenger aircraft and some restrictions on
domestic travel including cancellation of domestic passenger air
traffic); closing educational establishments, gyms, museums,
and theatres; bans on mass gatherings; and encouraging firms to
promote remote work or work from home. On March 24, 2020,
the Government of India under Prime Minister Narendra
Modi ordered a nationwide lockdown for 21 days from March
25, 2020 to April 14, 2020, limiting movement of the entire 1.3
billion population of India as a preventive measure against
the 2020 coronavirus pandemic in India. The announcement
came in the backdrop of the COVID-19 outbreak and is intended
to enable the concept of ‘social distancing’ to contain the spread
of the virus. The order of lockdown was issued under the
Epidemic Diseases Act, 1897 and Disaster Management Act,
2005 (Wikipedia, 2020).
5 Relief package to help fight the Covid-19 outbreak: Finance
Minister Nirmana Sitharaman on March 26, 2020 announced a
relief package worth Rs 1.70 lakh crore (valued at approximately
0.8 percent of GDP)to help the nation's poor tackle the financial
difficulties arising from Covid-19 outbreak. The economic relief
package will focus primarily on migrant labourers and daily
wage labourers. The package includes a mix of food security
and direct cash transfer benefits which shield poor families
during the lockdown (India Today Web Desk, 2020).
The key elements of the package are: in-kind (food;
cooking gas) and cash transfers to lower-income households;
insurance coverage for workers in the healthcare sector; and
Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide ...... 19
wage support to low-wage workers (in some cases for those still
working, and in other cases by easing the criteria for receiving
benefits in the event of job loss). These measures are in addition
to a previous commitment by Prime Minister Modi that an
additional 150 billion rupees (about 0.1 percent of GDP) will be
devoted to health infrastructure, including for testing facilities
for COVID-19, personal protective equipment, isolation beds,
ICU beds and ventilators. Several measures to ease the tax
compliance burden across a range of sectors have also been
announced, including postponing some tax-filing and other
compliance deadlines. Numerous state governments have also
announced measures to support the health and wellbeing of
lower-income households, primarily in the form of direct
transfers (free food rations and cashtransfers)—the magnitude
of these measures varies by state, but on aggregate measures
thus far amount to approximately 0.2 percent of India’s GDP
(IMF, 2020).
6 Measures taken by Reserve Bank of India: On March 27, 2020
the Reserve Bank of India (RBI) reduced the repo and reverse
repo rates by 75 and 90 basis points (bps) to 4.4 and 4.0 percent,
respectively, and announced liquidity measures to the tune of
3.7 trillion Rupees (1.8 percent of GDP) across three measures
comprising Long Term Repo Operations (LTROs), a cash
reserve ratio (CRR) cut of 100 bps, and an increase in marginal
standing facility (MSF) to 3 percent of the Statutory Liquidity
Ratio (SLR). Earlier in February, the CRR was exempted for all
retail loans to ease funding costs. The RBI has provided relief to
both borrowers and lenders, allowing companies a three-month
moratorium on loan repayments and the Securities and
Exchange Board of India temporarily relaxed the norms related
to debt default on rated instruments. At the same time, the
implementation of the net stable funding ratio and the last stage
of the phased-in implementation of the capital conservation
20 Arthshodh
buffers were delayed by six months. On April 1, the RBI created
a facility to help with state government's short-term liquidity
needs, and relaxed export repatriation limits. Earlier, the RBI
introduced regulatory measures to promote credit flows to the
retail sector and micro, small, and medium enterprises (MSMEs)
and provided regulatory forbearance on asset classification of
loans to MSMEs and real estate developers. CRR maintenance
for all additional retail loans has been exempted, and the
priority sector classification for bank loans to NBFCs has been
extended for on-lending for FY 2020/21. The RBI asked financial
institutions to assess the impact on their asset quality, liquidity,
and other parameters due to spread of COVID-19 and take
immediate contingency measures, including BCPs, to manage
the risks following the impact assessment (IMF, 2020).
7 Emergency Response and Health System Preparedness
Package: Government of India has announced significant
investments to the tune of Rs.15000 crores for 'India COVID-19
Emergency Response and Health System Preparedness
Package'. The funds sanctioned will be utilized for immediate
COVID-19 Emergency Response (amount of Rs.7774 crores) and
rest for medium-term support (1-4 years) to be provided under
mission mode approach. The key objectives of the package
include mounting emergency response to slow and limit
COVID-19 in India through the development of diagnostics and
COVID-19 dedicated treatment facilities, centralized
procurement of essential medical equipment and drugs
required for treatment of infected patients, strengthen and build
resilient National and State health systems to support
prevention and preparedness for future disease outbreaks,
setting up of laboratories and bolster surveillance activities, bio-
security preparedness, pandemic research and proactively
engage communities and conduct risk communication activities.
These interventions and initiatives would be implemented
Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide ...... 21
under the overall umbrella of the Ministry of Health and Family
Welfare. Ministry of Health & Family Welfare (MoHFW) is
authorized to re-appropriate resources among components of
the package and among the various implementation agencies
(National Health Mission, Central Procurement, Railways,
Department of Health (PIB, 2020).
Difficulties in implementing lockdown and Challenges faced by India while combating COVID-19
Following are some difficulties faced by admiration of central,
state and local government in implementing nationwide lockdown
and challenges faced by India while combating pandemic spread of
Coronavirus (IA Staff, 2020).
The mass exodus of migrant workers: The announcement of
21-day lockdown with little to no time for preparation forced
migrant workers, who travel to the cities for work to commute to
their home states. This lockdown coincides with the harvest season,
the time when the migrant workers seek harvesting jobs in large
states. To return to their homes, these migrant workers walked a
long distance. However, they were stopped by authorities, which led
to them being stranded in large masses. In response to this crisis,
shelters and food were provided for these migrant laborers by the
state government, voluntary organizations and NGOs. Yet, their
accommodations do not provide for social distancing.
The vulnerability of rural India: Majority of the Indian
population live in rural areas. In comparison with urban areas in
India rural areas in India has limited access to the healthcare system,
making it difficult for these people to be tested and treated. This, as a
result, makes it highly difficult to monitor the spreading of
infections in these areas.
The ambiguity of the term ‘essential items’: The Centre has
exempted ‘essential items’ manufacturing in its 21-day lockdown
notification. However, there is no clear definition of the term ‘essential
items’, leading to states having different views on what is essential.
22 Arthshodh
Unorganized Sector: The people who would face the worst
impact of the lockdown would be those relying on the unorganized
sector, which amounts to about three-fourths of India’s working
population. These individuals are vulnerable because they do not have
financial security due to the lack of jobs during the time of lockdown.
Challenges faced by India while combating COVID-19
Limited testing: During early stage of the outbreak, the Indian
Council for Medical Research (ICMR) only allowed testing of those
who have travel history and those who have come in contact with them
and then have gone on to develop symptoms to be tested for COVID-
19. This led to India having one of the lowest testing rates in the world.
Also, initial tests didn’t specifically test for COVID-19 but just any
strain of Coronavirus, including SARS and MERS. As of March 24,
2020, India has just tested 18 individual per million people.
Lack of strong and well equipped public healthcare: In India,
states control their public healthcare system. The biggest states are the
most vulnerable as their healthcare may be overwhelmed due to their
dense population. Though the majority of healthcare is provided by
private hospitals; which are generally better-run and better equipped,
is costly and inaccessible for many. Beside, Government hospitals in
India especially in rural areas are ill-equipped to handle this situation.
This means that India’s healthcare is not well equipped to deal with
Stage-3 of the COVID-19 infection.
India’s elderly population and population density: Around
100 million people in India are over the age of 60. This is the age
group that is the most susceptible to the infection. This figure is
higher than Italy’s population, the country that is worst hit due to the
pandemic. India’s population density is about 450 inhabitants per
square kilometer. China, which has the world’s highest population,
has a density of 150 inhabitants per square kilometer. About 120000
people share 1 km2 in Mumbai, which is 12 times more than New
York City’s population density. Also, one-sixth of the population lives
in the slums, making them even more vulnerable to the infection.
Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide ...... 23
Less number of Personal Protective Equipment (PPE) and
Ventilators: India has less number of PPE which provide protections
to medical professionals including doctors and nurses from
Coronavirus infections. Not only that there is a shortage of ventilators
also; which are useful to critical patients. As the number
of coronavirus (Covid-19) cases is on the rise in India, the central
government has estimated a steep rise in demand for personal
protective equipment (PPEs) and coronavirus diagnostic kit in the
coming months. The country would require an estimated 27 million
N95 masks, 15 million PPEs, 1.6 million diagnostic kits, and 50,000
ventilators by June 2020 (BS Web Team, 2020).
India’s growth rate projections by various national and
international rating agencies and institutions for the financial year
2020-21: Rating agencies, both national and international, are
unanimous that the COVID-19 pandemic will be an economic
catastrophe for India. Even though the country may not slip into a
recession, unlike the European countries, United States, or Asia-
Pacific that have stronger trade ties to China, analysts be certain of the
impact on India’s GDP growth will be significant. India is currently in
the midst of a 21-day lockdown that began on March 25, to contain
the spread of the coronavirus. The fallout of the move will spill over
to financial year 2020-21. In India, GDP growth is already at a decadal
low and any further dent in economic output will bring more pain to
workers who have seen their wages erode in recent times (Mathew,
P., 2020). Following table provide details of India’s growth rate
projections by various national and international rating agencies and
institutions for the financial year 2020-21.
Name of the rating agencies Growth rate projections for the
financial year 2020-21
Moody’s Investors Service, March 27, 2020 Reduced from 5.3% to 2.5%
Crisil, March 26, 2020 Reduced from 5.2% to 3.5%
Standard & Poor’s (S&P), March 31, 2020 Reduced from 5.2% to 3.5%
Fitch, April 3,2020 Reduced from 5.1% to 2.0%
24 Arthshodh
Name of the rating agencies Growth rate projections for the
financial year 2020-21
CARE Ratings Between 1.5 to 2.5%
KPMG, April 4,2020 Below 3%
Barclays, March 30, 2020 Reduced from 4.5% to 2.5%
India Ratings and Research (Ind-Ra), March 30,
2020
Reduced from 5.5% to 3.6%
Asian Development Bank, April 3, 2020 4%
Goldman Sachs, April 09, 2020 Reduced from 5.8% to 1.6%
Source: (Mathew, P., 2020), (PTI (1), 2020), (TPT Bureau, 2020), (BusinessToday.In.,
2020), (Noronha, G (1)., 2020), (Mukewar, P., 2020), (Kumar, C., 2020) , (Noronha,
G (2)., 2020) and (Mishra, P., 2020)
Based on India’s growth rate projections for the financial year
2020-21 by various national and international rating agencies and
institutions after the announcement of nationwide 21 day lockdown
due to the spread of Coronavirus; it is expected that Indian economy
will certainly and significantly slow down growth rates in near future.
Intensity and spread of Coronavirus to various parts of the country
viz. urban, semi urban and rural areas including metropolitan cities
and villages will significantly and adversely affects different segments
of the society and sectors of an economy. Government, economic
policy makers and planners have to formulate appropriate economic
policies and strategies to sustain and increase growth rates of
different sectors of an economy without compromising social welfare
of different segments and sections of society in days to come.
Impact of Coronavirus (COVID-19) on Demand and Supply
side as per survey conducted by FICCI
The rapid outbreak of deadly Coronavirus pandemic in the
country has not only led to a panic-like situation amongst the citizens,
but has also hit Indian economy - which was already reeling under a
significant slowdown over the past few quarters. The medical
rampant has presented fresh set of challenges for the country's
economy, causing severe disruptive impact on investment and
Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide ...... 25
consumption demand. India’s economy, which was growing at a six-
year low rate of 4.7 % in the third quarter of the current fiscal, had
strong hopes of recovery in the fourth quarter. However, the new
Coronavirus epidemic has made the recovery extremely difficult in
the near to medium term. According to a survey conducted by FICCI,
the outbreak has assembled new roadblocks for the Indian economy
now, causing severe disruptive impact on both demand and supply
side elements which has the potential to derail India’s growth story
(FICCI, 2020).
Impact of COVID-19 on demand side: As per FICCI
survey, tourism, hospitality and aviation are among the worst
affected sectors that are facing the maximum brunt of the
present Coronavirus pandemic. Closing of cinema theaters and
declining footfall in shopping complexes have affected the retail
sector by impacting consumption of both essential and discretionary
items. Consumption is also getting impacted due to job losses and
decline in income levels of people, particularly the daily wage
earners due to slowing activity in several sectors including retail,
construction, entertainment and others. With widespread fear and
panic rapidly increasing among people across the country, overall
confidence level of consumers has dropped significantly, leading to
postponement of their purchasing decisions. Even the travel
restrictions imposed by Central government to prevent the spread of
Covid-19 in India have severely impacted the transport sector.
Impact of COVID-19 on supply side: Large scale shutdowns
of factories and resulting delay in supply of goods from China
have affected many Indian manufacturing sectors. According to the
FICCI report, sectors like automobiles, pharmaceuticals, electronics,
chemical products etc. are facing an imminent raw material and
component shortage. Besides having a negative impact on imports of
important raw materials, the slowdown in manufacturing activity in
China and other markets of Asia, Europe and the US is impacting
India’s exports to these countries as well.
26 Arthshodh
Labour Market: The sudden displacement of migrant labour
would have far-reaching impact on the Indian economy and states
should be prepared to deal with the consequences of behavioural
changes forced by the lockdown. The sudden lockdown and the
consequent shutdown of transport created a humanitarian crisis in
many states as panic-stricken migrant workers took to the highways
trying to walk hundreds of kilometers home. A number of migrant
workers who fled the big cities may never return, preferring to eke
out a living on their marginal farms or find work in nearby towns. It
would deprive industrial centers such as Gurugram, Surat and
Tiruppur of labour for a long period of time, likely raising the wage
burden on small- and medium-sized units struggling to crawl out of
recession. The Economic Survey 2016-17 had estimated that at least
nine million people migrate annually within the country, most of
them in search of work. While the top destination for migrants is
Delhi, followed by Mumbai, the southern states have become a
migrant magnet in recent years. The largest number of them sets off
from Bihar, UP, Bengal and Assam, often traveling more than 3,000
km to distant Kerala. There may be a second wave of home-coming of
migrant workers once the lockdown is lifted. Many who decided to
stay back are desperately waiting for transport to be available. They
would take off at the first opportunity. That would mean even if those
who left earlier decide to return, companies may find a shortage of
labour. The disruption could extend to farms which may also feel the
shortage as the kharif sowing season begins with the rains
(Narayanan, D., 2020).
Problems faced by Micro Small and Medium Enterprises
(MSME): Along with tackling healthcare on a war footing, the
government will have to pay attention to the brewing economic crisis.
According to CII data in a report released last year, the MSME sector
added 13-15 million jobs annually. It is vital that this sector, a key
component of the Indian economy, be protected during times of crisis.
However, even if global economies bounce back sooner than
Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide ...... 27
expected, Indian MSMEs are likely to pay a high price. These
companies are too small to have enough of a cushion to last through a
pandemic like this one. Add to this the fact that many of these
companies have been asked to down shutters or curtail operations
while still paying employees and that’s apart from meeting costs for
taxes, power, and other utilities. In the wake of a pandemic like this
one, demand is likely to soar, while supply will be extremely weak.
Raw materials will likely be in short supply, as free trade will be
curtailed for a while. Wuhan, the center of the pandemic, is also one
of the largest auto hubs in the world. With Wuhan shut for months,
there’s going to be a huge shortage of components too. At this point in
time, China seems to have entered the post-peak period. According to
the WHO, this is when levels of the disease drop from the peak and
the process of recovery begins. Much of the rest of the world is still in
the early stages of the pandemic. This means China could get its
industries up and running in time to meet the global post-pandemic
demand. While that may be good news for a connected world, it
could be another severe blow to Indian MSMEs who manage to
survive. Given raw material, transportation, and labour issues that
manufacturers are likely to face, they are not going to be able to drop
their prices. China, with its head start, could still manage to get low-
cost products to the world, creating a massive competition issue for
Indian exporters (Mukewar, P., 2020).
Effects on Agricultural Sector: More than half of India's
workforce engages in farming, while agriculture contributes around
16% to the country's GDP. India is one of the world's largest producers
of crops like rice, wheat, sugarcane, cotton, vegetables and milk. The
coronavirus lockdown will have an adverse effect on the agriculture
sector in India.The sector is facing a lot of trouble with labourers and
movement of goods. As Rabi harvest season approaches, farmers
worry about their standing crops. Farmers growing wheat, mustard
and pulses already complained about their crops damage due to
untimely and heavy rainfall recently. This led to farmers fixing their
28 Arthshodh
crops but amid Coronavirus lockdown most of the labourers available
fled to their homes. As the restriction on movement of goods continues
amid the lockdown, the farmers are likely to feel the pinch in
their income. Moreover, farmers fear the sowing of summer season
crop as none of the shops selling seeds, fertilizers and other vital
inputs. Besides, several farm machines like combine and harvesters lie
stranded on highways as there is no one to operate them. Coronavirus
lockdown has impacted the supply chain of agricultural commodities.
By taking a toll on the loading and unloading of agricultural produce.
Also, the lockdown has hampered the movement of trucks carrying
essential commodities. Several cold storage and warehouse owners
complained regarding the dearth of laborers. Unwillingness to work
fearing police beating, many labourers are staying home or leaving for
their hometown. In all, the COVID-19 caused clampdown has caused
disruption and will eventually lead to a dip in farmer’s income (Kaur,
G., 2020). Central government will pay Rs 6,000 to each farmer in three
instalments in a year under the Prime Minister's Kisan Samman Nidhi
Yojana.
Government agri-research body Indian Council of Agricultural
Research (ICAR) is assessing the impact of Covid-19 lockdown on
agriculture and allied sectors and taking measures to minimize its
effect on the country’s food security. ICAR had issued crop-specific
advisories to farmers, asking them to take general precautions and
safety measures during harvesting, post-harvest operations, storage
and marketing of rabi crops. While the government has exempted
many agricultural operations from harvesting to movement of
produce to mandis from lockdown rules, the ICAR study will help the
government take further action (PTI (2), 2020).
Effects on Employment, Unemployment and Labour
Participation Rate: As per Centre for Monitoring Indian Economy
(CMIE) recent report; in March 2020, the labour participation rate fell
to an all-time low, the unemployment rate shot up sharply and the
employment rate fell to its all-time low. The employment rate fell to
Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide ...... 29
an all-time low of 38.2 per cent in March 2020. The fall since January
2020 is particularly steep - almost spectacular. It seems to have
nosedived in March after having struggled to remain stable over the
past two years. Then, there is a precipitous fall. The Labour
Participation Rate in March 2020 was 41.9 per cent. It was 42.6 per
cent in February and 42.7 per cent in March 2019. This fall in the LPR
in March was the result of a sharp 9 million falls in the labour force -
from 443 million in January 2020 to 434 million in March 2020. Fall in
labour participation rate is largely associated with the national
shutdown to contain the spread of Coronavirus. But, this fall seems to
have happened even before the lockdown. It gets much worse as we
move into the lockdown. The unemployment rate in March was 8.7
per cent. This is the highest unemployment rate in 43 months, since
September 2016. The unemployment rate during this last week of
March, 2020 was 23.8 percent. Labour participation rate fell to 39
percent and the employment rate was a mere 30 percent. These are
very big variations and are subject to the usual sampling errors. It,
therefore, may not be very wise to focus on the magnitude of those
movements but on the certainty of the movements (Vyas, M., 2020).
Effects on Inflation: On the price scenario, slowdown in
demand and production activities, a sharp fall in the global price of
crude oil, and price decreases in other major commodities such as
energy, base metals and fertilizers among others are expected to exert
downward pressure on inflation. Dun & Bradstreet (D&B) expects the
CPI inflation to remain in the range of 6.5-6.7 per cent and WPI
inflation in the range of 2.35-2.5 per cent during March 2020 (PTI (3),
2020).
Effects on Poverty: As described above spread of Coronavirus
(COVID-19) and 21 day nationwide lockdown will have significant
effects on various industries and sectors in days to come. This will
lead to reduction in employment rate and labor participation in
agriculture sector, MSME and other unorganized sectors in India. Due
to this it is expected that people living below poverty line and level of
30 Arthshodh
absolute as well as relative poverty will increase substantially. Recent
United Nations (UN) report has estimated about 400 million people
working in the informal economy in India are at risk of falling deeper
into poverty due to the coronavirus crisis which is having
‘catastrophic consequences’ (PTI (4), 2020).
Way ahead: The coronavirus pandemic and nationwide
lockdown have adversely affected various sectors and industries in
India. This comes at a time when India’s economy and public
finances were already under substantial stress. India must think
about how to deal with the public health crisis and rebuild its
economy once lockdown is lifted.
After lockdown; restarting requires accurate data on infection
levels. The government must strengthen public healthcare
infrastructure, particularly in smaller towns and villages.
Government ensures more and rapid testing, rigorous
quarantines, availability of masks and PPE kits to health
professionals. Appropriate measures to identify and contain
new infections should be adopted.
So far there is no medicine or vaccine to protect against infection
of Coronavirus, boosting immunity of people is one of good
alternative. Government should distribute immunity booster to
everyone. Considering regional and cultural diversity in India,
localized (house made) immunity booster recommended by
homeopathy or Ayurveda can be recommended.
The areas with no Coronavirus cases and no migration of
workers should allow all sectors and services to operate. The
public transport will be partially restored and limited
movement on roads will be allowed. However, considering
possibility of further spread of Coronavirus pandemic people
have been asked to avoid any unnecessary travel.
Considering nature of Indian economy and present situation
where millions of people who have lost their jobs especially in
Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide ...... 31
agriculture, MSME and unorganized sector. After the lock down
is lifted; more emphasis should be given to labour intensive
sectors from demand and employment perspective. Govt. can
boost scheme like MGNREGA and other employment oriented
programmes to enhance domestic demand. This will help
circulation flow in economy.
To boost agricultural sector farmers and farm labourers could be
allowed to work with reasonable safeguards as long as social
distancing norms are maintained. Harvesting season is on and
scarcity of labour has affected the agricultural sector; to ensure
maximum harvesting
MSME sector and small and tiny businesses may be affected
extremely by the ongoing lockdown and crisis. The government
should impose higher import duties on non-essential
commodities, raw materials and final product for a time being
which will give protection and boost to the domestic producers.
MSME sector should be given GST and Income tax relief for the
financial year 2020-21.
Government should provide low interest term loan to MSME
sector. Also, about six month extension should be given to
payment of existing EMI. This will help them to invest in this
difficult time and accumulate capital.
Government can expand the scope of MGNREGA by reducing
constraint of providing employment for stipulated days only.
For the time being this restriction can be removed. MGNREGA
workers should be used in agricultural sector for harvesting as
well as they will be provided employment in MSME sector
where higher skills are not essential. On one hand it will give
good grains whereas on another hand it will give employment
and wages to MGNREGA workers. This strategy will boost
demand and active supply chain in Indian economy.
32 Arthshodh
Direct transfers to households may reach most but not all, the
quantum of transfers seems inadequate to see a household
through a month. Government needs to ensure that the daily
wage earners, poor people and non-salaried workers will be
prevented from the pandemic of Coronavirus.
Significant and substantial reduction in growth rate projections
by various national and international rating agencies and
institutions will lead to demote in investor’s confidence that will
affect to a dropping exchange rates in this condition, and
substantial losses for our financial institutions. Limited fiscal
and financial resources are certainly a concern. Government
and Reserve Bank of India needs to revive entre financial system
for stimulating economic growth.
Government can collaborate more with NGO, philanthropists
and corporates; as they can play an effective role in creating
awareness among the public about the adverse effects of
Coronavirus infections. They are expecting to play a key role in
trying to contain the spread of Coronavirus and help in areas
such as patient care, support to governments, community
sensitization, hygiene promotion, distribution of food to needy
people and contact tracing. Government can also think to have
Public Private Partnership (PPP) mode for the same.
In restarting, e-commerce platforms and all its value chain
companies should be enabled so that supplies of essentials are
not affected, and people can stay at home.
In this situation, India can gain maximum benefit from
demographic dividend too. Healthy youth, stiff with
appropriate distancing at the workplace in aviation, hotel,
hospitality and tourism etc. for restarting.
Government should ensure to provide uninterrupted social
safety net to vulnerable sections of society along with orphans,
widows, elderly people, pensioners and persons with disabled
(PwD).
Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide ...... 33
Conclusion
So far India has one of the lowest testing rates in the world;
increasing the number of tests and rapid strengthening of health care
system is a need of the hour. The Indian economy is likely to suffer
for a long time as even once the situation is controlled, it will take a
long time for everything to come normal. Government, economic
policy makers and planners have to formulate appropriate economic
policies and strategies once when the lockdown is lifted (sooner or
later) to sustain and increase growth rates of different sectors of an
economy without compromising with the social welfare of different
segments and sections of society in days to come.
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34 Arthshodh
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36 Arthshodh
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Accounting Studies 37 Volume 11 No. 1 May, 2013
Cost Benefit Analysis of Tomato Cultivation Under Polyhouse in Haryana
Komal Malik
Vinay Kumar
Abstract
Tomato is one of the most extensively used and demanded vegetable crop because of its special nutritive value. Among all the vegetables, tomato is one of those few vegetables that remain in demand throughout the year. Therefore, the study was carried out with the objective of analyzing the viability of tomato cultivation under polyhouse in Haryana. Both primary and secondary data was used for conducting the study. Primary data was collected using a well structured questionnaire schedule. Purposive sampling was used to draw the sample in which a total of 180 farmers were choosen for the study. In order to analyze the results descriptive statistics like ratio, average and percentage were used. The study concluded that the cost benefit ratio for the crop of tomato cultivated under polyhouse is 1.32 which shows that tomato cultivation in polyhouse is viable for the farmers. Majority of the produce i.e. sixty percent of the tomato production was sold in regulated mandies. Therefore, it is necessary to continue research on Polyhouse so that viability can be increased and at the same time constraints can be wiped out.
Keywords: Polyhouse, Haryana, Constraints, Cost-Benefit.
Introduction
In the recent scenario, there has been a shift from traditional
farming towards horticulture farming. Among horticulture, the main
focus is upon vegetable crops as there demand is continuously
Assistant Professor, Department of Economics, Govt. College, Nalwa, Nagour,
Rajasthan. Assistant Professor, Department of Mathematics and Statistics, CCSHAU,
Hisar, Haryana.
38 Arthshodh
increasing among the consumers due to its nutritional value.
Tomato is considered as one of the most “protective food” due to its
nutritional value. Tomato (Lycopersicon esculentum) belongs to
the genus Lycopersicon under Solanaceae family. Tomato is a
good source of minerals, fiber, vitamins A, C and potassium. In
the Indian Culinary tradition, the most versatile and widely
used vegetable is tomato. Tomatoes are used for sauces, pickles,
puree, ketchup, soup and also as a salad. To various small and
marginal farmers, tomato acts as a good source of income. In
terms of production as well as area rank of India is second and
is after China. Haryana is one among the leading producer of
tomato as it stood at 12th rank with 392.36 thousand tonnes
(Anonymous, 2013) among all Indian States. In value addition
chain of processing tomato has very few competitors. Protected
cultivation is an improved agro technique being used worldwide to
register 3-4 times increase in production (Kumar, 2016). Tomato is
grown extensively in the plastic greenhouses for higher productivity
(Rana et al. 2014). Therefore, the present study has been conducted
with the following objectives:
1) To analyze the cost benefit ratio of cultivating tomato
under polyhouse in Haryana.
2) To analyze the marketing behavior of tomato cultivated
under polyhouse.
3) To find the major constraints of tomato cultivation under
polyhouse.
Materials and Methods
In order to conduct the present investigation, both primary as
well as secondary data has been analyzed. Primary data has been
collected through a well structured questionnaire schedule which
was then used for face to face interview for data collection. The
questionnaire was pre tested on a group of sample and the required
necessary changes has been incorporated into it. The questionnaire
Cost Benefit Analysis of Tomato Cultivation under Polyhone in Haryana 39
included both closed and open ended questions into it. Purposive
sampling was used to carry out the research. Districts of Bhiwani,
Sonipat and Rohtak were choose for study as these were the districts
having highest number of polyhouse in Haryana. A total of 180
farmers were choosen for the investigation. Secondary data was
collected from District Statistical Office and Department of
Horticulture, Haryana. The results of the study were analyzed using
Descriptive statistics like percentage, average, ratio etc. The gross
returns and net returns of tomato cultivation under polyhouse were
calculated by employing the following relationship.
GR = Yr.Pr
NR = GR – Total Cost
Where
GR = Gross returns per hectare of tomato in Rs/ha.
NR = Net returns per hectare of tomato in Rs/ha.
Yr = Yield of tomato in q/ha.
Pr = Price per quintal tomato in Rs/ha.
Results and Discussion
The per unit cost structure for tomato cultivation under
polyhouse is shown in the table1 formulated below:
Table 1: Per Unit Cost Structure of Tomato Cultivation ( /per acre)
S. No. Particulars Tomato (Cost Structure)
A Material Cost
1 Farm Yard Manure 9267.34
2 Seeds 25546.40
3 Plant Protection Chemical 13242.56
4 Fertilizers 5763.82
A-I Total Material Cost 53820.12
B Labour Cost
5 Land Preparation 5246.70
40 Arthshodh
6 Seed Bed Preparation & Sowing 5934.66
7 Irrigation 964.40
8 FYM & Fertilizer Application 1234.80
9 Hand Weeding 1236.50
10 Harvesting 52944.24
B-I Total Labour Cost 67561.30
11 Variable Cost( A-I + B-I) 121381.42
12 Interest on Working Capital 10317.42
13 Total Variable Cost ( 11+ 12 ) 131698.84
C Fixed & Other Cost
14 Rental value of land 30000
15 Interest on Fixed Capital 2850
16 Marketing Cost 16531.48
17 Management Charges 10726.64
C-1 Total Fixed &OtherCost 60108.12
Total Cost ( 13 + C-I) 191806.96
Source: Primary Survey
As the total cost of production for tomato was divided into
two parts; one was total variable cost and other one was total fixed
and other cost. Total variable cost has been further sub divided into
two major parts in which one was material cost and second one was
labour cost. The major portion of expenditure in material cost section
was spend upon seeds which constitutes an amount of 25546.40 per
acre. The highest expenditure among labour cost was made on
harvesting which were 52944.24 per acre. The addition of the total
labour cost and total material cost was variable cost. The variable
cost for the production of tomato under the polyhouse cultivation
was an amount of 121381.42 per acre. If we add the interest on
working capital an amount of 10317.42 per acre to the variable cost
Cost Benefit Analysis of Tomato Cultivation under Polyhone in Haryana 41
calculated above, we get the total variable cost. The total variable
cost for tomato cultivation was 131698.84 per acre.
Among the fixed cost the maximum expenditure was upon
rent paid for the land. The average value of rent per acre was about
thirty thousand which was highest among all expenditures. One of
the probable reasons for such a high rental value of land was the
prosperity of Haryana state among the agriculture sector. The
summation of total variable cost and total fixed and other cost gives
us the total cost of tomato cultivation in polyhouse which was an
amount of 191806.96 per acre.
1.1. Return Structure of Tomato Cultivation
The per unit return structure of tomato cultivation in
polyhouse is shown in table2. The total yield that was obtained from
the field measuring one acre was 16.33 ton’s. The average price in
the market for the tomatoes which were cultivated in polyhouse
was 15.50 per kilogram.
Table 2: Per Unit Returns Structure in Tomato Cultivation ( / Acre)
Sr. No Particulars Return Structure
1 Total Yield(ton’s) 16.33 (rounded off)
2 Price (per kg) 15.50
3 Total Returns 253185.19
4 Total Cost 191806.96
5 Net Returns ( 3-4) 61378.23
C:B Ratio 1.32
When we multiply the total yield obtained from the field with
the per kg price, we get the total returns an amount of 253185.19
per acre for the tomato from the polyhouse. By subtracting the
amount of total cost i.e. 191806.96 from the total return of
253185.19, we got the net returns for the crop of tomato i.e.
61378.23 per acre. The table reveals that the cost benefit ratio for
42 Arthshodh
the crop of tomato cultivated under polyhouse is 1.32 which shows
that vegetable cultivation in polyhouse is beneficial for the farmers.
1.2. Marketing of Tomato
Marketing is one of the vital component that determines the
viability of any vegetable crop as if there are proper marketing
channels avilable for the product it will fetch higheramount of
remuneration to the farmer’s and hence higher level of profit. Therefore
the most considerable part is marketing aspect in the agriculture sector.
Table 3: Marketing channel for sale of produce of Tomato
Sale of the Produce Percentage
On Farm 4%
In Local Village Market 6%
Regulated Market / Mandi 60%
Sale in Retail Stores (i.e. Easy Day, Reliance Fresh, Grofers etc.)
11%
Hotel (on contract base) 19%
Processing Industry Nil
Other if any (specify) Nil
Total 100%
The table 3 formulated above shows the marketing channel
for the tomatoes cultivated in polyhouse. The table revealed that
four percent of the total produce was sold on the farm itself. Out of
the total produce six percent was sold in the local village market.
Next come was the regulated markets or mandis in which the
highest percentage was sold out as it constitutes for sixty percent of
the entire produce. Out of the total cultivated crop eleven percent
was sold to retail stores. The remaining nineteen percent was sold to
the hotels on the contract basis in which they make some informal
kind of contract with the farmers to take a required quantity on a
daily basis. Nothing was supplied to the processing industries.
1.3. Constraints of Polyhouse
Less durability of polyhouse cladding material and lack of
skilled labour were major production related constraints while
Cost Benefit Analysis of Tomato Cultivation under Polyhone in Haryana 43
unorganized marketing system, lack of vegetable processing units, lack
of suitable cold storage facilities and lack of transportation facilities
were some major marketing related constraints faced by the farmers
cultivating tomato crop in polyhouse.
Conclusions
The importance of cost of cultivation cannot be ignored as it is
vital component in determining the viability of cultivating tomato
under polyhouse. The study concluded that the cost benefit ratio for
the crop of tomato cultivated under polyhouse is 1.32 which shows that
vegetable cultivation in polyhouse is viable for the farmers. Majority of
the produce i.e. sixty percent of the tomato production was sold in
regulated mandies which is a matter of concern as it will longer the
chain between the producer and the consumer which needs to be
reduced. Therefore, it is necessary to continue research on Polyhouse so
that viability can be increased and at the same time constraints can be
wiped out. New options for better marketing can be explored like
creation of special mandies for polyhouse crops, food processing
industries which directly purchase the crop from the farm. A lot has
been done so far and a huge remains to be explored and improved.
References
1. Cheema, D.S., Kaur, S., Srinivasan, R. and Kaur, S. (2010):
Monitoring of major pests on cucumber, sweet pepper and
tomato under net-house conditions in Punjab, Pest
management in horticultural ecosystems, 16(2): pp.148-155.
2. Gnanasekaran A. and Vijayalakshmi S. (2014): Economic
analysis of tomato cultivation in Dindigul district of Tamil
Nadu, International Journal of Science and research, 3 (12),
pp. 995-997.
3. Hazarika T.K. and Phookan D.B. (2005): Performance of
tomato cultivars for polyhouse cultivation during spring
summer in Assam, Indian Journal of Horticulture, 62(3):
pp.268-271
44 Arthshodh
4. Joseph A. and Muthuchamy I. (2014): Productivity, quality and Economics of Tomato (Lycopersicon esculentum Mill.) cultivation in aggregate hydroponics – A case study from Coimbatore region of Tamil Nadu, Indian Journal of Science and Technology, 7(8), pp. 1078-1086.
5. Kumar P., Chauhan R.S. and Grover R.K. (2016): Economics analysis of tomato cultivation under poly house and open field conditions in Haryana, India. Journal of Applied and Natural Science., 8(2): pp.846 – 848.
6. Lekshmi S.L. and Celine V.A. (2015): Evaluation of tomato hybrid for fruits, yield and quantity traits under polyhouse conditions, International Journal of Applied and Pure Science and Agriculture, 1(7), pp.58-62.
7. Murthy D. S., Prabhakar B. S., Hebbar S.S., Sreenives V. and Prabhakar M. (2016): Economic feasibility of vegetable production under polyhouse: a case study of capsicum and tomato, Journal of Horticulture Science, 4 (2):pp.148- 152.
8. Parvej M.R., Khan M.A.H. and Awal M.A. (2010): Phenological Development and Production Potentials of Tomato under Polyhouse Climate, Journal of Agricultural Sciences, 5(1): pp.19-31.
9. Rana, N., Kumar, M., Walia, A., Sharma S. (2014). Tomato Fruit Quality under Protected Environment and Open Field Conditions, International Journal of Bio-resource and Stress Management, 5(3): 422-426.
10. Sepat N.K., Sepat S.R., Sepat S. and Kumar A. (2013): Energy use efficiency and post analysis of tomato under green houses and open field production system at Nubra valley of Jammu and Kashmir, International Journal of Environmental Sciences. 3(4), pp. 1233-1241.
11. Wani K.P., Singh K.P., Amin A., Mushtaq F. and Dar Z.A.(2011): Protected cultivation of tomato, cucumber and capsicum under Kashmir valley condition, Asian Journal of Science and Technology, 1(4), pp. 56-61.
Accounting Studies 45 Volume 11 No. 1 May, 2013
Growth Analysis of Area, Production and Yield of Rapeseed and Mustard Crop in Rajasthan
Preeti Prasad
Rashmi Bhargava
S.K Kulshrestha
Abstract
Rajasthan State is the desert dominant state even role of agriculture
plays a vital role in the economy of the state. Rajasthan is the higher producer
of mustard since it is a major crop of the state. The mustard crop covers
almost all districts even desert districts of the state so the area and production
have enhanced over the years in the state of this crop. Rajasthan has first
place in the area as well as the production of rapeseed-mustard crop and has
second place in productivity after Haryana. This paper deals with the area,
production, and yield growth of rapeseed-mustard crops over thirty-five
years. This paper shows that most of the districts of the state have positive
and significant growth in area, production, and yield of the crop.
Introduction
Mustard seeds were obtained from Channu-Daro of Harnapan
civilization. 2300–1750 B.C. (Allchin 1969). The Aryans used Brassica
species as spices and for oil. Thus it is clear that over a period of more
than 3500 years, mustard came to occupy an important place in the diet
of the Indian people as a source of oil and vegetation.
Research Scholar, Department of Economics, S.P.C Government College,
Ajmer, M.D.S.University. Ajmer, Rajasthan. Associate Professor, Department of Economics, S.P.C Government College,
Ajmer, Rajasthan. Assistant Professor in Economics, Vardhman Mahaveer Open University,
Kota, Rajasthan.
46 Arthshodh
The estimated area, production and yield of rapeseed-
mustard in the world was 36.59 million ha (mha), 72.37 million
tonnes (mt) and 1980 kg / ha respectively, during 2018–19. Globally,
India accounts for 19.8% and 9.8% of total production and
production (USDA). During the last eight years, productivity has
increased significantly from 1840 kg / ha in 2018–19 in 2010–11 and
production has also increased from 61.64 tonnes in 2010–11 to 72.42
million tonnes in 2018–19.
India is the fourth largest oilseed producing country in the
world, next only to USA, China and Brazil, harvesting about 25
million tons of oilseeds against the world production of 250 million
tons per annum. Since 1995, Indian share in world production of
oilseeds has been around 10 percent. Although, India is a major
producer of oilseeds, per capita oil consumption in India is only 10.6
kg/annum which is low compared to 12.5 kg/annum in China, 20.8
kg/annum in Japan, 21.3 kg/annum in Brazil and 48.0 kg/annum in
USA (Report on GPDP Project in Edible Oil Industry in India).
In Rapeseed-mustard, India has ranks second both in the
production (6.82 Mt) as well as in the area under cultivation (6.27 M ha)
of rapeseed-mustard in the world. Rapeseed-mustard is a rabi crop
predominantly grown in the states of Rajasthan, Uttar Pradesh,
Madhya Pradesh and Haryana. These states together contribute 4.90 M
ha of the area and produce 5.60 Mt of rapeseed-mustard. It is mainly
used as edible oil and medicine for burning. Its use is limited for
industrial purposes owing to high cost. Rajasthan is the leading
rapeseed-mustard producing state though its share has declined in
recent years. The production, area and yield of rapeseed-mustard seed
experienced a significant growth from 1985-1995, primarily due to the
increase in irrigated land and the availability of high-yielding seeds in
the country. This trend was partly reversed due to intermittent famine
conditions in some of the major rapeseed-mustard producing states,
such as Rajasthan. Jha et. al presented the comparison of area,
production and yield of mustard crop as in table 1.
Growth Analysis of Area, Production and Yield of Rapeseed .......... 47
Table 1: Period-Wise Growth Rates (%) In Area, Production and Yield of Major Rapeseed-Mustard Crop-Producing States: 1980-81
To 2008-09
State Area (M ha) Production (Mt) Yield (kg /ha)
Rajasthan 6.17 7.96 1.69
Uttar Pradesh -2.75 0 2.82
Haryana 4.62 5.43 2.05
Madhya Pradesh 3.73 6.61 2.53
Other states 1.26 2.25 1.32
India 3.87 4.18 2.26
Source: Jhaet. al (2012)
Objective of the study
The objective of the paper is to identify the growth rate of
area, production and yield during the period of study.
Literature Review
Jhaet. al (2012) analyzed that the major oilseed-producing
states, Rajasthan, Madhya Pradesh and Maharashtra have exhibited
the healthy growth rates in the area, production and productivity
during 1980-2009. Only a few states like Haryana, Madhya Pradesh,
Maharashtra, Rajasthan and West Bengal have increased the oilseeds
production through both area as well as productivity improvement.
Hedge (2012) estimated that projected Indian population of
1685 million by 2050, 17.84 Mt of vegetable oils is required to meet
the fat nutrition. This is equivalent to roughly 59.41 Mt of oilseeds. If
one assumes 25% of vegetable oils from crops other than annual
oilseeds, then the country needs to produce just 44.56 Mt of oilseeds
by 2050 to meet fat nutrition of the projected population. With full
adoption of currently available oilseed technologies, this level of
production could easily be achieved.
Kumrawat and Yadav (2018) proved the enhancement of
mustard crop in Bharatpur region over the years. Jain et. al (2016)
describes that fluctuating yield for oilseeds crops and the area and
yield instability of the mustard crop has been found declining overtime
48 Arthshodh
plausibly because of increase in irrigation facilities, location-specific
technologies and better input management.
Methodology
This study based on the time series data of area, production and
yield from the year 1980-81 to 2014-15 of district level. The secondary
data is collected from Agricultural Statistics of Rajasthan published by
the Directorate of Economics & Statistics, Rajasthan. The semi log
model and coefficient of variation have been used here to estimate the
district wise growth rate of area/ production/ yield in Rajasthan.
Semi-Log: The exponential equation is given by LnYi = α + βt + Ui This is fitted using OLS method
Here Yi = Area/Production/Yield inithyear (i= 1, 2, 3…N) α = Intercept, β = Regression coefficient, Ui = Residual term The parameters α and β are estimated by the least square
method. The collective effect of all explanatory variables on explained
variable is denoted by R2. It is called determination of coefficient.
R2 = 1-
The coefficient of determination (R2) has also been calculated
for the model.
The coefficient of determination (R2) has also been calculated
for the model.
Analysis of Area, Production and Yield
Rajasthan state has noticed highest growth rate in rapeseed –
mustard crop in term of area and production. It is clear from table 2
that production of rapeseed and mustard increased over the year in
most of the districts of Rajasthan. It is obvious that the highest growth
in production is found in Jaisalmer, Jhalawar, Kota, Bundi and
Jhunjhunu respectively. In which Jaisalmer, as well as Jhalawar growth
is 24 per cent and 23 per cent per annum and these are also statistically
significant. There are few districts in which growth is not statistically
significant although they have positive such as Ajmer, Jalore, Pali and
Growth Analysis of Area, Production and Yield of Rapeseed .......... 49
Sirohi district. Banswara district has negative growth during the period
of study which is minus four per cent. The area of rapeseed/mustard
crop has been increased over the year in the state since this is a most
edible crop not only in state but also in country so most of the farmers
are growing this crop. Many districts are such as Jaisalmer, Bundi,
Kota, Tonk and Churu are among the highest growing area district of
the crop. All district has been noticed positive growth except Banswara
where were negative growth rate in the area. There are some districts
where the growth rate in the area of this crop is near to zero such as
Pali, Sirohi and Udaipur. The yield of the mustard not much increased
during the period of study as compare to production and area. Some
district has three per cent growth rate such as Sikar, Jhunjhunu, Barmer
and Kota. Other district has growth rate less than 3 per cent. Some
districts have negative growth rate in such as Ajmer and Jaisalmer.
There is a district where the productivity growth rate near zero such as
Churu but it is not statistically significant. Some other district like
Bhilwara, Tonk and Bikaner where the growth rate is not statistically
significant.
Table 2 Production, Area and Productivity Growth of Rapeseed/ Mustard
Districts
Production Area Productivity
Growth Coefficient
R2 Growth
Coefficient R2
Growth Coefficient
R2
Ajmer 0.05(0.0672) 0.11 0.06(0.009) 0.22 -0.02(0.016) 0.18
Jaipur* 0.09(0.000) 0.6 0.08(0.000) 0.55 0.01(0.051) 0.13
Sikar 0.09(0.000) 0.74 0.06(0.000) 0.7 0.03(0.000) 0.44
Jhunjhunu 0.11(0.000) 0.79 0.08(0.000) 0.72 0.03(0.000) 0.53
Alwar 0.06(0.000) 0.77 0.04(0.000) 0.63 0.02(0.000) 0.48
Bharatpur 0.05(0.000) 0.62 0.03(0.000) 0.43 0.02(0.000) 0.45
Dholpur 0.05(0.000) 0.6 0.03(0.000) 0.42 0.02(0.001) 0.34
S.Madhopur* 0.08(0.000) 0.57 0.06(0.000) 0.54 0.02(0.003) 0.28
Bikaner 0.08(0.000) 0.61 0.06(0.000) 0.71 0.02(0.103) 0.09
Churu 0.11(0.000) 0.56 0.11(0.000) 0.65 0(0.956) 0
Ganganagar* 0.07(0.000) 0.74 0.05(0.000) 0.7 0.02(0.000) 0.36
Jodhpur 0.08(0.000) 0.75 0.05(0.000) 0.62 0.02(0.000) 0.5
Jaisalmer 0.24(0.000) 0.75 0.26(0.000) 0.81 -0.02(0.016) 0.25
Jalore 0.03(0.0552) 0.13 0(0.6973) 0.01 0.02(0.000) 0.57
Barmer 0.060.0018 0.3 0.05(0.006) 0.24 0.01(0.050) 0.13
Nagaur 0.07(0.000) 0.46 0.04(0.002) 0.29 0.03(0.000) 0.48
Pali 0(0.9283) 0 -0.01(0.631) 0.01 0.01(0.079) 0.11
Sirohi 0.01(0.6725) 0.01 0(0.909) 0 0.01(0.061) 0.12
Kota* 0.14(0.000) 0.72 0.11(0.000) 0.62 0.03(0.000) 0.72
50 Arthshodh
Bundi 0.12(0.000) 0.61 0.12(0.000) 0.6 0.02(0.004) 0.26
Jhalawar 0.23(0.000) 0.65 0.2(0.000) 0.61 0.02(0.003) 0.34
Tonk 0.11(0.000) 0.59 0.11(0.000) 0.51 0.01(0.101) 0.09
Banswara (-0.04(0.0026) 0.34 -0.08(0.000) 0.59 0.02(0.000) 0.5
Dungarpur 0.02(0.5398) 0.02 0(0.942) 0 0.02(0.015) 0.25
Udaipur* 0.04(0.023) 0.17 0.01(0.377) 0.03 0.02(0.000) 0.39
Bhilwara 0.08(0.0002) 0.41 0.09(0.000) 0.45 0.01(0.192) 0.06
Chittorgarh 0.1(0.000) 0.48 0.09(0.000) 0.41 0.02(0.003) 0.27
State 0.75(0.000) 0.75 0.05(0.000) 0.63 0.015(0.006) 0.31
Source: Authors’ Calculation
It is clear from the figure 1 that the growth rate in production
of mustard is different across the districts, some district growth rate
for the production of mustard is much higher such as Jaisalmer and
Jhalawar district, on the other hand, some districts have negative
growth rate like Banswara.
Fig 1 Production Growth of Rapeseed and Mustard
The figure 2 show that the area under the rapeseed/mustard
crops have growing trends in most of the districts of the state.
Jaisalmer is one of the districts where the area increased the fastest
growth rate among all districts. The reason being is that Indira
Gandhi takes over the district over the years. Agriculture potential
other districts such as Jhalawar, Kota, Bundi and Churu etc. increase
in this crop over the period of study. There are also some districts
where the area under this crop has not been increased such as
Dungarpur, Jaloreand Sirohi. A few districts where the growth rate
of the area has been negative in term of growth such as Banswara
Growth Analysis of Area, Production and Yield of Rapeseed .......... 51
and Pali. It is clear from the figure that the farmer is interested in
this type of crop in Rajasthan.
Fig.2 Area Growth of Rapeseed and Mustard
It can be drawn from figure 3 that the productivity of mustard
has been increased at the rate of two percent most of districts of the
state. The two districts such as Ajmer and Jaisalmer over the years.
There are four districts such as Jhunjhunu, Kota, Nagaur and Sikar
where the growth rate of yield three per cent per year.
Fig. 3 Productivity Growth Rate of Rapeseed and Mustard
Conclusion
This paper shows that there has been a good increase in both
area and production of mustard crop in different districts of
Rajasthan, but the growth rate in productivity is however positive
52 Arthshodh
but it has increased comparatively less. It has also been found from
this paper that despite Rajasthan State being a Desert State, both the
area and production of Mustard have increased in Desert districts.
Therefore, it can be said that this crop is being grown in good
quantity in all the districts except a few districts. Due to the
contribution of most districts to Mustard production, Rajasthan state
is also the first place in Mustard production in the entire country.
References
1 Jha, Girish Kumar, Suresh Pal, V C Mathur, GeetaBisaria, P
Anbukkani, R R Burman and S K Dubey(2012): “Edible Oilseeds
Supply and Demand Scenario in India: Implications for Policy”,
Indian Agricultural Research Institute, Director, IARI.
2 Nethrayini, K.R. and Mundinamani, S.M.(2013). Impact of Technology
Mission on Oilseeds and Pulses on Pulse Production in Karnataka.
International Research Journal of Agricultural Economics and
Statistics 4(2): 148-153.
3 Sharma, A.K., and Thomas, L.,(2013). Technology inputs and its
impact on farm profits: A case study of rapeseed mustard. Indian Res.
J. Ext. Edu. 13(3): 9- 14.
4 Swain, M., Problems and Prospects of Oilseeds Production in
Rajasthan: Special reference to rapeseed & mustard, AERC Report
submitted to Ministry of Agriculture, Government of India, New
Delhi (2013).
5 Kumrawat Meena and Yadav Manju(2018).Trends in Area,
Production, and Yield of Mustard crop in Bharatpur Region of
Rajasthan, International Journal of Engineering Development and
Research, Volume 6, (1), 315-321.
6 P.K. Jain, I.P. Singh and Anil Kumar(2005).Risk in Output Growth of
Oilseeds in the Rajasthan State: A Policy Perspective. Agricultural
Economics Research Review, Vol. 18 (Conference No.) 2005, 115-133
Accounting Studies 53 Volume 11 No. 1 May, 2013
Inequality Re-examined Amidst Covid-19
Dr. G.L. Meena
Abstract
The policies of Liberalization, Privatization and Globalization put
India on the path of Higher Growth Rate (HGR) from the Hindu Growth
Rate(HGR). Then the important question at hand was had India been able to
distribute the benefit of that higher growth among its population evenly?
Value of Gini index being constantly higher signifies that the benefit of
growth has been reaped by a few only and around 176 million people are still
living below poverty line. It seems this inequality is not even realized during
the normal course of life however the hard times like Covid-19 uncovers the
difference in the standard of living of people. The main aim of the present
study is to put forth such inequalities prevailing in the day to day life of
Indian people particularly in the light of Covid-19. It seeks to examine the
inequalities based on gender, education, region and sector of employment.
The study finds that these inequalities are interconnected and cannot be
separated from each other. Thus, the policy makers do not seem to live up to
the expectations of the constitution makers where equality of status and of
opportunity had been envisaged to promote among all the citizens.
Keywords: HGR, Inequality, Covid-19.
Introduction
It is said that “charity begins at home” invigorated by that, an
unwarranted similar term can be coined for Indian societyi.e.
“inequality beginsat home”. According to the CIA World Factbook the
Assistant Professor, Dept. of Economics, University of Rajasthan, Jaipur,
(Rajasthan).
54 Arthshodh
value of Gini index, considered as a standard measure of inequality in
income distribution, was 35.2 for India in the year 2011 which is higher
than the other neighbouring countries like Pakistan, Bangladesh and
Nepal all being behind the country in economic growth. It clearly
signifies that India has not been able to distribute the benefits of higher
growth even equally among its population if not progressively.
Another unpleasant information lending support to this exclusionary
growth can be viewed in the percentage of people living below poverty
line. World Bank Poverty & Equity Brief states that between FY2011-12
and 2015 India witnessed a drastic decline in the poverty at the
international poverty line from 21.6 to 13.4 percent. However, the
absolute aspect of the picture narrates a different story as the number is
crossing 176 million. No society can claim to be poverty and inequality
free because these concepts are relative, however one can envisage a
decent life defined in terms of realisation by every citizen the
constitutional provisions of right to dignity, equality and livelihood
security. COVID-19 led situation has invoked resumption of such long-
overlooked discussions built on sharing of national prosperity, a decent
life ensured by constitutional provisions, exercising equality of rights
and unilateral decision making in a democratic state. As soon as the
lockdown was placed in by the central government of India a large
pool of stranded workers was on roads and then only many eyes
glared through a new India completely unsecured, in search of life
rather than livelihood. All of sudden it was realised that poverty and
inequality may be reducing over the years in degree but not in kind
and intensity. This revived many hidden questions like are we living in
a country which is divisive at various fronts ranging from home to
workplace? Is state doing justice with its role in securing life and
livelihood of people particularly in hard times of Covid-19? Do we see
appropriate participation by different strata of the society as agents in
policy formulation? All such pertinent questions necessitate a re-
examination of inequality prevailing in India at different levels.
Inequality Re-examined Amidst Covid – 19 55
In the quest of finding answers to these questions, the presents
study is an attempt to re-examine the inequalities prevailing in the
Indian society at different levels in the light of Covid-19 outbreak. After
introduction, second section presents an account of the data set used in
the study. Section third, being the core part of the study, provides a
detailed discussion of inequality in general and specifically with
reference to Covid-19. The final section concludes the study.
Data and Methodology
The main aim of this study is to put forth inequalities prevailing
in the day to day life of Indian people and although it’s not possible to
quantify each aspect of inequality. However, an attempt has been made
here to bear a pragmatic approach in analysing the issue to the possible
extent. The study is based on secondary data source. To serve that
purpose, various reports have been consulted like NSSO rounds on
Employment and Unemployment, Annual Report PFLS 2017-18, India
Wage Report -ILO, Report on Fifth Annual Employment-
Unemployment Survey, World Bank Poverty and Equity Brief to name
a few. Due to unavailability of data for the period mentioned, it was
not possible to stick to a unique reference period. Moreover, focus of
the study is more on the nature and kind of inequalities prevailing in
India rather than on its degree. Therefore, the reference periods,
deviating merely for 4-5 years, does not seem to make a difference to
the conclusions. In the present study the time period of 2011 has been
considered wherever census data is used and on the other hand
reference period of 2017-18 stands for NSSO data. This deviation is
permissible on the grounds that during this period no structural break
had been realised by Indian economy.
Inequalities in India
3.1 Gender imbalance: one of the major inequalities
prevailing in India can be seen in gender disparity. Constitutional
provisions impart equal status to women however that has not been
practiced so far. Both as a home maker as well as a factor of
production, their services have been undermined over the years. It
56 Arthshodh
will not be wrong to say that the life of an Indian woman begins
with the household chores while theman’swith the newspaper and a
cup of tea. Quantitative as well as qualitative aspect of gender
disparity in India presents a sad state of affairs. It will not be wrong
to say that quantitative aspect portrays the society’s psyche towards
women while qualitative one throws light on their being recipient of
unequal treatment in their life span. Using some most representative
parameters, an account of their state has been demonstrated below.
3.1.1 Quantitative Aspect:
To exhibit this aspect of gender disparity, sex ratio has been
used as a proxy since it directly deals with the number of females
which is measured in terms of 1000 males. Figure :1 depicts that sex
ratio is below 1000 in every state of the country except Kerala.
According to census 2011, male-female ratio in India is 1.06 males for
every female which is higher than the international average of 1.01.
the more striking feature is that child sex ratio (among 0-6 years age
group) is even worse with the figure of 1.09.
S
Source: Census of India, 2011.
A differenced sex ratio has been reflected by figure:2 where
the difference between the overall sex ratio and child sex ratio has
been found to be negative for all states except the five. it clearly
Inequality Re-examined Amidst Covid – 19 57
implies that preference for male child has been increasing over the
time. It also cements the belief that this gender imbalance will persist
even in the times to come.
Source: Census of India, 2011.
One of the interesting facts is that the first child not being
male is one of the most critical factors in determining sex ratio. How
it ultimately affects the sex ratio is determined by the educational
attainment level of the parents in turn. In educated family tendency
is such that if first child happens to be a male, it negatively affects
sex ratio however nothing can be said explicitly for less educated or
uneducated families. Conversely, first child not being male has high
probability of affecting sex ratio positively irrespective of the nature
of families in terms of education. Although it’s not the result of their
desire to balance sex ratio rather of their preference to have male
child at any cost. It clearly hints at a social construction founded on
gender biasness, discriminating against women. Number based
discrimination is not as big an issue as the kind of treatment females
receive in India because sex ratio is imbalanced for almost each
country of the world with varying degrees. Therefore, an analysis of
qualitative aspect of gender disparity is required.
58 Arthshodh
3.1.2 Qualitative Aspect:
Here, by qualitative aspect we mean do the females enjoy the
benefits of equal status through their course of life? This has been
assessed by using three indicators reflecting the nature of status i.e.
literacy rate as a signal of social status, representation in assemblies
measuring their role in policy making and average level of wage rate
to check their status in economic spheres.
Figure-3 provides a picture of the gender gap in the literacy
rate in India where the excess of male literacy rate over the female
literacy rate has been shown. It can be easily noticed that the figure
is positive for all states of the country. Even at the national level,
male literacy rate is considerably higher than the female literacy rate
and it is around 17 percentage points. More than 30% females being
illiterate is a sign of their deprivation from the basic right of
education. It undermines their role in decision making and confines
them up to the household chores. Low female literacy is also one of
the critical factors causing high population growth in a already
highly populated country.
Source: Census of India, 2011.
Second important indicator is the one which registers the role
of women in political spheres and that also defines their role in policy
making. As mentioned above, it has been measured as in terms of
Inequality Re-examined Amidst Covid – 19 59
their representation in legislative councils of Indian states and of the
country as a whole meaning that in state assemblies and in parliament
particularly in Lok Sabha. Figure:4 demonstrates that representation
of women in the state assemblies is very low, putting that in numbers
the highest percentage is 14 for three states i.e. Bihar, Haryana and
Rajasthan. Surprisingly these are the three states where literacy rate of
females is the lowest, sex ratio is the lowest and the differenced
literacy rate is the highest. Although it’s hard to find any correlation
between these two contradictory facts. Overall representation of
women in state assemblies is around 9 percent. There are two states,
Mizoram and Nagaland, where not a single woman is a part of
assembly. At national level, in the 17thLoksabha elections the
percentage of elected women representatives is around 14.3 % which
is very low not only in comparison of developed countries as shown
in table 1 but unfortunately also below the neighbouring countries
like Pakistan and Bangladesh. It is also at subpar with the other
member countries of BRICS. Thus, the results are alarming in the
sense that women’s role in policy making in India is less than the
countries which are at the advanced stage of development, the
countries which are lagging behind India in growth terms and the
countries which are almost at par with India in terms of growth. That
simply means status of women is more concerned with the societal
norms than with the economic prosperity.
Source: Chapter-5 Participation in Decision Making, Men and Women in India, 2017. pp:103-104.
60 Arthshodh
Table 1: Representation of Women in National Parliaments (Lower House)
(in %) India 14.3
Developed Countries
Italy 35.7
UK 32.0
Australia 30.0
US 23.6 Neighbouring Countries
Nepal 32.7
Pakistan 20.2
Bangladesh 20.7
Bhutan 14.9 BRICS
Brazil 15.0
Russian Federation 15.8
China 24.9
South Africa 42.7
Source:http://archive.ipu.org/wmn-e/classif.htm
One pleasant aspect of the picture is that 73rd amendment of
Indian constitution has mandated reservation of seats for women in
Panchayat Raj Institutions’ (PRIs)Elections also termed as local
elections. 20 states have made the provisions of 50 percent
reservation to women in PRIs. However, the crucial point here is to
see whether that mandate has been followed in letters only or in
spirit as well. Here a pragmatic approach suggests that the former
has overshadowed the latter which is exemplified in the stature of
women representative elected as head of the last unit of grassroot
level administration known as Sarpanch. The reality is that their
being elected as Sarpanch has evolved a new concept of “Sarpanch
Pati” (husband of sarpanch) where all her duties and powers are
exercised by her husband furthering his dominance over his wife
specifically and women in general in the power structure. The step
was welcome but implementation would remain poor until the
missing element from the policy making is realised and worked
Inequality Re-examined Amidst Covid – 19 61
upon. Awareness generation and capacity building are the two most
important instruments in strengthening the position of any
vulnerable section of the society and equally important is their
timing. No doubt women’s position has been strengthened in the
documents but their literacy rate remains a big issue to be dealt with.
Government has put in efforts to build up their capacity however,
proper attention has not been paid to their education. Had the
Sarpanch been educated they would have discharged their duties at
their own discretion and better enjoyed the power.
The third important indicator highlights the relative economic
status of women and it has been measured by gender wage gap. It’s
worthwhile to mention here that the gender wage gap is defined as
the difference between median earnings of men and women relative
to median earnings of men. Figure 5 presents a sketch of the gender
wage gap in India. It is found to be 39% in the year 2011-12 which is
quite higher however, the positive side of the picture is that it has
been declining persistently from 1993-94. Although this gender wage
gap is “raw or unadjusted” meaning that the variables affecting the
wages from the background has not been controlled here. Education
level, age and other skills are the key factors in deciding the level of
wages thus the comparison without adjusting those variables cannot
said to be fully scientific.
Source: India Wage Report ILO, 2018.
62 Arthshodh
Therefore, the next figure:6 has been produced here which
deals with the difference in average level of daily wages gender wise
and that have also been categorised according to the employment
status so that the difference in the endowments can be better
captured. Figure manifests the considerable difference between the
average wages of the male and female across all the categories of
employment where wages of former exceeds the latter.
Source: India Wage Report ILO, 2018.
In addition to these facts, World Economic Forum published a
report on Global Gender Gap in 2020 which put forth some more
dimensions of the gender inequality. The report came up with the
calculation of global gender gap index along with the four more sub-
indices and one of them is the economic participation & opportunity
index which quantifies the economic status of women. In figure 7 it
is easily visible that there is a negative relationship between the
economic participation & opportunity and proportion of unpaid
works per day. Unpaid works has been measured as the proportion
of hours of work spent by female at home in care and volunteer
work to the hours of work spent by men for the same activities. In
India, this ratio has been found to be around 10 meaning that
women spend 10 times much time in these activities as done by men
Inequality Re-examined Amidst Covid – 19 63
leading to decline in their share in economic activities. Eventually
value of sub index for India is 0.354 and stands at 149th place among
the 153 countries of the world. The report has rightly figured out
that one of the dominant causes of gender inequality in India is the
fact that a major contribution is made by women in household
chores for which they are not paid. According to the report female
estimated earned income is mere one-fifth of maleincome, which is
also among the world’s lowest (144th).
Figure : 7 Economic Participation and Time Spent in Unpaid Domestic Work
Source: Global Gender Gap Report 2020, World Economic Forum. pp-14.
Above analysis made in terms of quantity as well as quality
clearly indicates that the condition of women in India is precarious.
3.2 Education Led Inequalities
Another side of inequality is the one that a person comes
across as soon as he comes in contact with the outer world by joining
the educational institutions. Education system in India gives rise to
the dynamic inequalities meaning that it appears as if the education
system is not only emanating the inequalities but also increasing
those. The most important trait of such inequality is that it is
64 Arthshodh
institutionalised. Two type of educational institutes exist in India:
Public and Private. Public sector run schools are basically either of
Hindi medium or of regional language while most of the schools in
the private sector provide vernacular education. If the discussion is
extended to the senior secondary level institutes, the results are quite
interesting in the sense that the students coming from the public
sector institutes will either become a part of skilled labour or
unskilled labour. If they become part of skilled labour force then
language medium will not erect any barrier in their becoming part of
organised formal sector. However, the bigger question is how much
is the share of this organised formal sector in total employment.
Table 3 indicates that the share of organised formal sector
employment in the distribution of total employment was 7.2 % in the
year 2011-12 and it has witnessed a minor increase of mere 0.7
percentage points in the span of six years from 2011-12 to 2017-18.
On the other hand, remaining ones, who could not become
part of organised formal sector, end up becoming part of unskilled
labour force. Needless to say, howinsecure the livelihood of these
labourers is. it’s not that the students from the private institutions
never face unemployment however their medium of language
qualifies them for several sophisticated jobs of private sector. If they
prefer organised formal sector jobs, they have the better
opportunities there asshown by their number in that sector. For
instance, figure:8 highlights the gap in selection between the
students who write UPSC exams in the two mediums (Hindi and
English) in India which are considered as the top most
administrative services in hierarchy. Figure depicts that the gap
between the Hindi and English medium students has been widening
continuously during the period of 10 years while the gap between
the total number of students and the students from English medium
has been reducing. Gap between the English medium and Hindi
medium students, who qualified for the same exam, was equal to
735 in the year 2008 which rose to 8233 in the year 2018.
Inequality Re-examined Amidst Covid – 19 65
Figure 8: Medium of writing of Examination of CandidatesAppeared in Civil Services (main) Examination.
Nu
mb
er
of
Stu
den
ts
18000
10000
8000
6000
4000
2000
0
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Year
Total
English
Hindi
2018
12000
14000
16000
Source: Annual Reports of Union Public Service Commission.
Disparity in the education system reflecting through the
difference in the medium of language is sufficient to realise that
education system itself is creating inequalities in the country.
3.3 Rural-Urban Gap
Another type of inequality pervasive in the country is based
on the region. Here regional disparity does not stand for the gap
between two states or two districts but for the gap between rural and
urban area. More than 60 % of the total population of India resides in
the rural areas but when it comes to the gap in the facilities available
in the rural and urban areas, the state of affairs is dismal. To present
a rough sketch of this gap, some indicators have been chosen here
showing the socio-economic status of the rural and urban life in
India. To reflect the four dimensions of development seven
indicators have been chosen and out of them five are positive and
two are negative. Higher value of positive indicator shows higher
level of development, on the other hand higher value of negative
indicator reflects lower level of development. For instance, average
infant mortality rate and % of households with kuchha house are
66 Arthshodh
two negative indicators in the sense that their value being higher is a
sign of backwardness. Following this notion, the observation of table
2 confirms the belief that “India lives in its villages but not the
facilities/amenities”. In case of all positive indicators, urban area
exceeds the rural area with a considerable margin and conversely,
rural area supersedes the urban for negative indicators and again
with the substantial difference. The issue of grave concern here is
that all chosen indicators represents the basic requirements for the
survival and their lack demands a quick action to justify the term
“Inclusive” prefixed to the development.
Table 2: Rural- Urban Gap in India (2011)
Dimensions of development
Indicator Rural Urban
Standard of living Average Daily Wages 175 384
Education Literacy Level 68.91 84.98
Health Average Infant Mortality Rate 48 29
Access to basic amenities
% of Households with electricity 55.7 93.1
% of households with toilet 17.6 52.8
% of households with improved source of drinking water
84.5 95
% of households with a Kachha House
19.1 2.5
Source: Census 2011 and India Wage Report ILO, 2018.
3.4 Formal-Informal Sector gap in India
The biggest example of inequality, Covid-19 has also played a
vital role in highlighting that, is the difference found in the returns of
the factors of production particularly between the two main factors
namely capital and labour as well as the difference between the two
categories of labour itself engaged in informal and formal sector. It
can also be termed as inter-factor and intra-factor inequality.
Workers engaged in the informal sector are with no written job
contract, paid leaves, health benefits and social security. Even in the
organised sector out of 13.2%, 5.2% labour is of informal nature
Inequality Re-examined Amidst Covid – 19 67
signalling at the rapid informalisation of the organised sector. Its
illusionary as it is indicative of the expansion of organised sector
however does not show its informalisation and such workers in the
organised sector has been termed as “contractual workers”. The table
given below briefs the employment structure in Indian economy.
Table 3: Distribution of Total Employment in India (%)
Worker 2011-12 2017-18
Unorganised Organised Total Unorganised Organised Total
Informal 82.6 9.8 92.4 85.5 5.2 90.7
Formal 0.4 7.2 7.6 1.3 7.9 9.3
Total 83.0 17.0 100.0 86.8 13.2 100.0
Source: Murthy, Ramana S V (2019). Measurement of Informal Economy-
_Indian Experience, IMF Seventh Statistical Forum. p.03.
Table indicates that the employment has expanded both in
the informal as well as formal sector however there is a considerable
degree of difference in the expansion as the former exceeds the later.
The worst part of the picture is that informal workers engaged in
unorganised sector not only constitutes more than 80% of the total
labour force but also has been increasing during 2011-12 to 2017-18.
The most striking thing is that this is the most vulnerable part of the
labour force. An in-depth analysis of the composition of informal
workers reveals that its major part belongs to the self-employed and
casual labour amounting to 51.9% and 27% respectively in 2017-18.
A closer look at the difference of the wage rate between the
formal and informal sector workers presents a sad state of affairs.
The real average daily wage of the organised sector worker was 430
in the year 2004-05 which grew to 513 in 2011-12 and on the other
hand the real wages accruing to the unorganised sector workers
during the same periods were 109 and 166 respectively. One of the
worst parts of the informalisation can also be viewed in the
distribution of informal workers by their status of employment. For
instance, regular wage/salaried workers are supposed to have some
68 Arthshodh
element of security in their jobs. However, their working conditions,
shown in the table below, tell a different story:
This is the workforce working in the informal sector and
which was stranded during Covid-19. Particularly this was the
casual labour and some own account workers & helpers in
household enterprises. As soon as the lockdown was placed in, they
were turned (forced) out of their jobs. Covid-19 has also highlighted
one more type of inequality prevailing in the country i.e. there has
always been a huge gap in the earnings of capital owners and the
wage earners. Here, the role of state also needs to be scrutinised
particularly in the context of Covid-19. On the one hand,
government has announced packages for the industries while on the
other hand the states are let free to unilaterally amend the labour
laws. It clearly indicates that in lieu of narrowing down gap between
the earning of capital and labour, state is stretching that gap.
Conclusion
The main aim of the present study is to highlight the
inequalities prevailing in the Indian society at various levels from
home to workplace. These inequalities have always been an inherent
part of life in the country however the hard times of Covid-19 has put
them forth in the public domain and suddenly made it a part of public
discussion. The important point is that the inequalities discussed in
the study are interconnected and cannot be separated from each
other. Education led inequalities are closely linked to the region-based
disparity and that in turn results in gap between formal and informal
sector employment. It’s not unfair to say that there exists a vicious
circle of inequality in the country and Covid-19 has just drawn the
attention of everybody towards it. Stranded labour on the roads
striving for their lives, puts a question mark over the spirit of
preamble to the constitution of India where equality of status and of
opportunity had been envisaged to promote among all the citizens. It
seems that labour is asking to the constitution makers and to the
government “are we the people of India”?
Inequality Re-examined Amidst Covid – 19 69
References
1 Ghosh, Madhusudan (2017). Infrastructure and Development in
Rural India. Margin-The Journal of Applied Economic
Research 11:3, pp.256-289.
2 International Labour Organization (2018). India Wage Report:
Wage Policies for Decent Work and Inclusive Growth.
3 Jensenius, Francesca R. (2016). Competing Inequalities? On the
Intersection of Gender and Ethnicity in Candidate Nominations in
Indian Elections. Government and Opposition, 51(3), 440-463.
doi:10.1017/gov.2016.8.
4 Labour Bureau (2016). Report on Fifth Annual Employment -
Unemployment Survey (2015-16). Ministry of Labour and
Employment, Government of India.
5 MoSPI (2017). Women and Men in India-2017: Participation in
Decision Making. Ministry of Statistics and Programme
Implementation, Government of India.
6 MoSPI (2017). Selected Socio-Economic Statistics: India 2017.
Ministry of Statistics and Programme Implementation,
Government of India.
7 Murthy, Ramana S V (2019). Measurement of Informal Economy
Indian Experience. IMF Seventh Statistical Forum.
8 Narayanan, Abhinav (2015). Informal Employment in India:
Voluntary Choice or a Result of Labour Market Segmentation?
Indian Journal of Labour Economics 58, pp.12-16.
9 NCEUS (2007). Report on Conditions of Work and Promotion of
Livelihoods in the Unorganised Sector. National Commission for
Enterprises in the Unorganised Sector, Government of India.
10 PLFS (2019). Annual Report, Periodic Labour Force Survey July
2017-June2018. Government of India: Ministry of Statistics and
Programme Implementation, National Statistical Office.
70 Arthshodh
11 The World Factbook, Central Intelligence Agency (CIA), US.
Retrieved from https://www.cia.gov/library/ publications/
the- world-factbook/rankorder/2172rank.html.
12 World Economic Forum (2020). Global Gender Gap Report 2020.
13 The World Bank (2020). Poverty & Equity Brief, South Asia,
India. Retrieved Fromhttps://databank.worldbank.org /data
/download/poverty/33EF03BB-9722-4AE2-ABC7-
AA2972D68AFE/Global_POVEQ_IND.pdf
Accounting Studies 71 Volume 11 No. 1 May, 2013
Climate Change and Its Impact on Agricultural Production: An Evidence from India
Dr. Chitra Choudhary
Sumedha Bhatnagar
Abstract
In recent years, climate change is much talked about theme on
various platforms. Due to the drastic transformation in climate in past 26
years in form of change in rainfall pattern and temperature it has become a
major area of concern for many governmental organizations and policy
advisors as it impacts the development of an economy, directly or indirectly.
Climate change can be measured through the change in various factors
including rainfall pattern, temperature change, change in precipitation,
carbon emissions etc. Since agriculture sector that is the major driver of
any economy, specifically developing and less developed countries, is
largely dependent on the weather conditions of that country, change in
climate adversely impact agriculture and economy of that country. This
paper examines the impact of climate change on the agriculture production
of India. It also attempts to analyze the relationship between the climate
variables and the agricultural GDP on the country.
Key words:- Climate Change, Agricultural Production, Rainfall,
Temperature, Carbon-di-oxide.
Assistant Professor, Department of Economics, University of Rajasthan, Jaipur.
(Rajasthan). Research Scholar, Department of Humanities and Social Sciences, Malaviya
National Institute of Technology Jaipur, Rajasthan.
72 Arthshodh
Introduction
India is a developing economy and its economic development
is dependent on agriculture to a large extent for livelihood,
employments and overall economic growth (Stern 2006; Gollin, 2010).
After liberalization India faced acute famine and food scarcity for a
long period (Panagariya, 2005). Green revolution was the result of
persistent joint efforts that made the country self-sustainable and self-
reliant (Patnaik, 1990). Today, India is among the top exporters of rice
and other food grains (Dey, 2020). Geographically, India lies in the
South of Asia, due to its geographical positioning the overall climate
of the country is characterized as tropical in nature. According to
Koppen System, India hosts six major climatic subtypes that ranges
from arid desert in the west to the humid tropical region in the
southwest and island territories to alpine tundra and glaciers in the
north. The country faces varied, and unpredictable weather
conditions all around the year and extreme weather conditions can
possibly prevail in the country at the same point of time in two
different regions.
In recent years, climate change has been much talked about
theme on various platforms (Boutabba, 2014; Lobell and Asseng, 2017;
Guntukula, 2020). Due to the drastic transformation in climate in past
26 years in form of change in rainfall pattern and temperature it has
become a major area of concern for many governmental organizations
and policy advisors as it impacts the development of an economy,
directly or indirectly (Adams et. al 1990). Climate change can be
measured through the change in various factors including rainfall
pattern, temperature change, change in precipitation, carbon
emissions etc. (Auffhammer et. al, 2006; Auffhammer et. al, 2012).
Since agriculture sector that is the major driver of any economy,
specifically developing and less developed countries, is largely
dependent on the weather conditions of that country, change in
climate adversely impact agriculture and economy of that country
(Mendelson et. al 2006; Stern 2006; Kanwar, 2006; Fishman, 2011).
Climate Change and Its Impact on Agricultural Production : An ........ 73
According to the fourth assessment report of the Inter-
governmental Panel on Climate Change (IPCC), agriculture
production, including access to food, in many African countries is
projected to be severely compromised, it states that by 2020 the yield
from rain-fed agriculture could be reduced by up to 50%. Even
though, this is the debatable conclusion it, on broader view, it
demonstrates the risk to the crop yield of African countries due to
climate change. According to a report of Asian Development Bank
(2009), Asia and Pacific Regions have witnessed the rise in
temperature, this will impact the agriculture sector of this region as
37 percent of the total world emissions from agriculture production
are accumulated from Asia and the Pacific. The precipitation in India
is expected to rise by 10%- 12% and mean yearly temperature is
expected to rise by 3-6°C by 2100 (IPCC, 2014). The climate change is
likely to impact the agriculture production with expected fall of 10-
40% by 2100 (Agarwal, 2008). Even today, when technological
innovations and upgradation has taken place in the agriculture
sector, rainfall is the major factor that determines the overall
agriculture production of India thus highlighting the impact of
climate changes on the Indian economy (Lahiri and Roy, 1985; Sen
and Robert, 2004; Krishnamurthy, 2012).
The paper analyzes the impact of climate change into two
sections. First, it analyzes the impact on climate change variables on
the agriculture output and second, it studies the long-run
relationship between climate variables and agriculture GDP.
Additionally, the paper highlights the influence of fertilizer
consumption on agriculture output.
The paper is divided into 7 sections. Section 1 is the introduction
and it gives the brief overview of Indian agriculture and climate change
scenario. Section 2 is the literature review where the previous studies
are reviewed and based on which research gap is identified. The
section 3 is the methodology followed by findings of the study in
section 4. Section 5 and 6 gives the results and conclusion of the study.
74 Arthshodh
The last section gives the future scope of research and limitation of the
present study.
Literature Review
The economic impact of climate change on agriculture has been
studied extensively the world over and it the one debatable research
problem for the researchers (Mendelsohn et.al 1994; Auffhammer et.
al, 2006; Auffhammer et. al, 2012; Siddiqui et. al 2012). Climate change
can impact the economy in two ways; first by impacting the
production side of the economy and second by policy induced
abatement activities (Nordhaus, 1994). The effect of climate change on
the agriculture production was first studied by Arrehenius (1896),
who speculated the impact of concentration of atmospheric carbon
dioxide on the global temperature on the ground.
The impact of climate change on agriculture can be analysed in
two ways namely; through crop simulation approach (agronomic
simulations) that includes production function approach and
econometric modelling that also includes Ricardian analysis
(Mendelsohn et.al 1994; Sarker, et. al, 2014; Guntulula and Goyari
2020).The agronomic-economics is the study of physical impact that
are in the form of yield change and/or area changes, of climate
change. Whereas, in Ricardian approach farmers are assumed to
identify instantaneously and perfectly any change in climate, evaluate
all associated changes in market conditions, and then, modify their
action to maximize profits (Mendelsohn and Nordhaus, 1999; Seo et.
al 2008). Such assumptions are only viable in case when ergodic
agricultural system- i.e. where space and time are substitutable.
Schlenker (2006) estimated the impact of climate change on
the crop yield in the agriculture sector of the USA. The study
concluded that the increase in temperature impacted the farm value
but was nullified by the increase in agriculture production caused
due to high precipitation in the region. Thus, global warming in the
USA has very little impact on the agriculture sector. This,
highlighting that beginning of climate change may have small effect
Climate Change and Its Impact on Agricultural Production : An ........ 75
for developed countries but in future the impact has tendency to
multiply. The agriculture sector of developing and underdeveloped
countries is more sensitive to the climate variability (Kumar and
Parikh, 2001). The latitudes are also one of the factors that impact of
climate change on a country Stern (2006). Paul, et. al (2009) identified
that there is a possibility that agriculture sector may harm the
climate of the region as, 14% of nitrite oxide and methane comes
from agriculture sector and another 18% is produced due to
deforestation for agriculture use.
In India, Kumar and Parikh (2001), applied a variant of
Ricardian approach and stated that 2° Celsius temperature rise and
7% increase in rainfall would lead to almost 8 % loss in farm level
net revenue. The study further highlighted that under doubled
carbon dioxide concentration, in later half of 21st century, the GDP
tends to decline by 1.4% to 3 % due to climate change. Kumar (2003)
extended the analysis to include climate variation in Ricardian
approach and estimated that 5% increase in climate variation along
with other climate change scenario would result in almost 10% drop
in the farm level net revenue.
To account social interaction between farmers, Polsky (2004)
introduced a spatial econometric specification of the Ricardian
model. Whereas, Schlenker et. al (2005) used spatial features to
arrive at efficient estimators of regression parameters. Both the
authors supported the importance of inter-farmer communication
and its significant influence on climate sensitivities.
Tobey et.al (1992), in his study concluded that even with
concurrent productivity losses in the major grain producing regions
of the world, global warming may not cause widespread disturbance
in the world agriculture sector. The interregional adjustments in the
production and consumption will serve as a buffer to the severity of
climate change impacts on the world agriculture and result in
relatively small impacts on domestic economies. According to Kaiser
and Crosson (1995), the impact of climate change at the farm level
76 Arthshodh
will depend on the magnitude of change in climatic variables and on
how successfully farmers adapt to the new climate.
Lahiri and Roy (1985) studied the supply response of rice
yields at the all India level and also included monthly rainfall. They
also argued that with the spread of HYVs post-mid 1960s (1965
onwards) Indian agriculture has become more rainfall dependent
has the water requirement has gone up and the spread of irrigation
has not been in pace with it. The study was further extended to other
foodgrains by Kanwar (2006) he studied the supply response using a
state level dataset and agreed with previous study that rainfall
considerably matters for the supply response. Auffhammer et al
(2012) and Auffhammer et al. (2006) explicitly studied the impact of
too little/ too much rainfall (akin to gamma rainfall) on the rice
yields using the state level panel dataset.
A study by Siddiqui et. al (2012) attempted to investigate the
impact of climate on four major crops of Pakistan namely; wheat,
rice, cotton and sugarcane. It incorporated scientific information on
the stages of development of each crop in order to assess the impact
of climate change on each stage of the crops. Fixed effect model was
applied to study the impact of variables on crop productivity. The
study concluded that the impact of change in temperature and
precipitation varies significantly with the timing and production
stages of the crop.
Gupta, Sen & Srinivasan (2012) estimated the impact of
climate change on foodgrain yields in India, namely rice and milltes.
They estimated a crop-specific agriculture-production function with
exogeneous climate variables. They eschewed crop simultation
approaches that rely on experimental data. The results stated that
change in rainfall and temperature pattern significantly impacted
the production of rice whereas for milltes, rainfall is the sole
determinant for the change the production pattern.
Climate Change and Its Impact on Agricultural Production : An ........ 77
Guiteras (2009) examines the impact of temperature and
rainfall on combined yield (in money terms) for five major food and
one cash crop he found that climate can reduce yields by 4.5 to 9 per
cent in the medium-run (2010-39) and by as much as 25 per cent in
long-run (2070-2099) in the absence of long-run adaptation. His study
was criticized by Sarker et al. (2012) and by Krishnamurthy (2012) for
combining different crops which are differently impacted by climate
change. Fishman (2011) examined the district level panel and showed
the impact of intra-seasonal variability of rainfall on yields. He
concluded that the impact of climate change can be moderated by
spread of irrigation, but the effect varies with groundwater depletion.
Adams et al. used the output from two different climate
change models (Goddard Institute of Space Studies (GISS) and
Princeton Geophysical Fluid Dynamics Laboratory (GFDL) models)
to stimulate the agronomic and economic impacts of climate change
due to a doubling of atmospheric carbon dioxide. the results from
the study was contradictory to other studies, it found that acreage in
the southern United States generally decreased, while the acreage of
the Great lakes and northern Plains generally increased due to
climate change. It overstated the negative and/or understated the
positive economic impacts of the change.
The literature review highlights various models and
approaches adopted to analyse the impact of climate change on
different regions and different crops. A limited number of studies
have been conducted to empirically investigate the impact of climate
change on agriculture income in context of a developing countries
like India. Hence, present study is an attempt to empirically
investigate the impact of climate variables on the overall agriculture
production (GDP) of India during the period of 1960-2014.
Objectives and Hypothesis
Objectives:
1. To examine the impact of climate change on agriculture
production
78 Arthshodh
2. To observe the relationship between variables of climate
change and agricultural GDP
Hypothesis:
1. Climate change variables i.e. rainfall, temperature and carbon
dioxide emission have significant influence on agriculture
output.
2. A positive impact of rainfall and a negative effect of
temperature and carbon dioxide emission on the agriculture
output (AGDP).
3. Consumption of fertilizer has a significant influence on AGDP
Data Sources and Methodology
Data Source
Present study utilizes secondary and time- series data for the
analysis. The data has been congregated from the database of World
Bank. The time period of the study is from 1960-2014 and the variables
incorporated in the present analysis have been detailed as following:
(a) Dependent Variable-
Agricultural Gross Domestic Production (AGDP)
(b) Independent Variable-
Annual Temperature (Temp.)
Annual Average Rainfall (Rain.)
Carbon-di-oxide emission (Ce)
Consumption of Fertilizer (Cf)
Agriculture production is in crore rupees with the constant
price of the base year 2011-12. Temperature is in terms of degree
Celsius; carbon emissions are in terms of million tonnes and
fertilizer consumption is calculated in terms of kilo tonnes.
Methodology
The paper outlines that the agricultural production of a nation
is mostly influenced by variety of climatic and non-climatic
determinants. The main hypothesis is that the climate change
Climate Change and Its Impact on Agricultural Production : An ........ 79
variables i.e. rainfall, temperature and carbon dioxide emission have
significant influence on agriculture output. A positive impact of
rainfall and a negative effect of temperature and carbon dioxide
emission on the agriculture output (AGDP) is hypothesized.
Additional hypothesis is that consumption of fertilizer also has a
significant influence on AGDP. After discussing the interaction of
various factors, it tries to explore the impact of climate change
variables on agricultural production and carries out time series
regression analysis. The study employs time-series regression
analysis on the selected variables
(i) Specification of the Working Model
In this study an attempt has been made to establish
relationship between agricultural gross domestic production and
climatic variables through following model;
AGDPt= α + β1 Tempt + β2 Raint+ β3 Cet+ β4 Cft+ εt
Where,
α = constant or intercept term
t = time or trend variable
εt = independent and identically distributed residual term
Before fitting the model, stationarity of time series variables is
checked using the unit root test.
Check for Stationarity of Variables
The approach to unit root testing implicitly assumes that the
time series that is to be tested can be written as:
Yt= Dt + zt+ εt
Where,
Dt=deterministic component (trend, seasonal components etc.)
zt = stochastic component
εt = stationary error process
The aim is to determine whether the stochastic component
contains a unit root or is stationary
80 Arthshodh
Given a time series data, Augmented Dickey-Fuller (ADF)
considers three differential-form autoregressive equations to detect
the presence of a unit root:
Yt is a random walk:
∆Yt = γYt-1 +
Yt is random walk with drift:
Yt is a random walk with drift around a stochastic trend:
Where,
t is the time or trend variable
α is the intercept constant called a drift
β is the coefficient on the time trend
γ is the coefficient presenting process root, i.e. the focus of testing
p is the lag order of the first difference autoregressive process
is an independent identically distributed residual term
The difference between the three equations concerns the
presence of the deterministic elements α (a drift term) and (a
linear time trend). The focus of testing is whether the coefficient γ
equal zero that infers the original series has a unit root.
Further, in order to observe relationship among above
described variables Joahnsen Cointegration Test is used.
Johansen Cointegration Test: Given a set of I (1) variables
{Xit…Xkt}. If there exists a linear combination of all variables with
vector β so that,
… Trend stationary
Then the x’s are cointegrated of order C (1, 1)
Climate Change and Its Impact on Agricultural Production : An ........ 81
Cointegration in this paper is tested using Johansen
cointegration test also known as Johansen and Juselius (JJ) test. It has
two test statistics to check cointegration among the variables namely,
trace test and maximum Eigen value test. Trace test has a null
hypothesis that there are at most r cointegration vectors and
maximum Eigen value has a null hypothesis that there are r+1
cointegration vectors versus there are r cointegration vectors.
Findings
In order to regress the variables, stationarity test has been
conducted on all the variables. The ADF test for unit root is applied
for the statistical analysis. Schwarz Info Criterion (SIC) is used to
determine lag-length.
Table 1: Results of Unit root Test (source: author’s computation)
Augmented Dickey-Fuller Test
Variables Level/ First &
Second Difference
Without Trend With Trend
(t-
value)
(p-
value) (t- value)
(p-
value)
AGDP Level 4.76 1.00 0.71 0.99
First Difference -9.24 0.00 -11.93 0.00
Second Difference -6.71 0.00 -6.88 0.00
Temperature Level -1.48 0.54 -6.11 0.00
First Difference -10.79 0.00 -10.68 0.00
Second Difference -7.11 0.00 -7.08 0.00
Rainfall Level -7.77 0.00 -7.83 0.00
First Difference -8.72 0.00 -8.64 0.00
Second Difference -6.97 0.00 -6.95 0.00
Carbon
emission
Level 3.55 1.00 2.42 1.00
First Difference 0.25 0.97 -1.68 0.75
Second Difference -13.19 0.00 -13.40 0.00
Fertilizer Level 3.84 1.00 -0.63 0.97
First Difference -5.48 0.00 -5.94 0.00
Second Difference -5.38 0.00 -5.31 0.00
Ho is variable has a unit root
P<0.05 null hypothesis is rejected
82 Arthshodh
It is clear from the ADF results that AGDP and consumption
of fertilizer both are non-stationary at level and have stationary
process at first and second difference. Except level (without
intercept), temperature is found to be stationary in all cases. Rainfall
follows a stationary process at all levels. Carbon emission remains
non-stationary at level and at first difference, although becomes
stationary at second difference. In order to run a regression, all the
variables should be stationary at same level, hence analysis has been
carried out using variables at second difference.
Impact of Climate Variable on Agriculture GDP
After testing the stationarity of all the variables, time series
regression analysis is carried out to observe the impact of
independent variables (climate variables) on dependent variable
(AGDP). The result of simple regression is shown in table 2.
Table 2: Time-Series Regression Results
(Source: author’s computation) Dependent Variable: D (AGRICULTURE_GDP_IN_CRORE,2)
Variable Coefficient Std. Error t-Statistic Prob.
C 1424.963 2933.956 0.485680 0.6294
D (TEMPERATURE, 2) -16220.60 6164.654 -2.631226 0.0114
D (RAINFALL, 2) 972.5941 143.7659 6.765123 0.0000
D (CARBON EMISSION, 2) -0.141561 0.061842 -2.289069 0.0265
D (FERTILISER_CONSUMPTION, 2) 2.868969 3.074775 0.933066 0.3555
R-squared 0.652232 Mean dependent var 235.6717
Adjusted R-squared 0.623251 S.D. dependent var 34688.04
S.E. of regression 21291.48 Akaike info criterion 22.85959
Sum squared resid 2.18E+10 Schwarz criterion 23.04547
Log likelihood -600.7791 Hannan-Quinn criter. 22.93107
F-statistic 22.50574 Durbin-Watson stat 3.069413
Prob(F-statistic) 0.000000
Source: Computed
It is clearly reflected by the results that all the regressors have
expected hypothesized sign as described in methodology. Although,
except consumption of fertilizer all the climate change variables i.e.
temperature, rainfall and carbon emission are found to be significant.
Temperature has a negative impact on agricultural output, revealing
Climate Change and Its Impact on Agricultural Production : An ........ 83
that if it goes by one degree then the agricultural output will decline
by Rs. 16220 crores. The coefficient value of rainfall shows a positive
impact, depicting that one additional milliliter(ml) of rainfall will
cause agricultural output to increase by Rs.972 crore. Carbon emission
has a negative influence on output from the agricultural sector i.e. on
an average an increase of one-unit emission of carbonwill cause
agricultural output to decrease by Rs. 0.14 crore. The results also
highlight that impact of fertilizer consumption is significant in the
model the plausible reason for the insignificance of the consumption
of fertilizer could be its underestimated figures.
Overall model fitness is suggested by F-statistics. Value of R2 is
65.22, exhibiting that 65 per cent of the variation in the regressand can
be explained by the regressors in the model. Diagnostic test shows
that there is no problem of heteroscedasticity, and multicollinearity.
Residuals are also normally distributed. Although, there exists a
problem of autocorrelation, but its magnitude is not much.
Long-Run Relationship between climate variables and agriculture GDP
Johansen Cointegration test has been applied for finding the
long-run relationship among the all climate variables and
agricultural GDP.
Table 3: Johansen Cointegration Test
Series: AGRICULTURE_GDP ANNUAL_TEMPERATURE
AVERAGE_RAINFALL CO2_EMISSION
Lags interval (in first differences): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.448062 62.82518 47.85613 0.0011
At most 1 * 0.284159 31.92054 29.79707 0.0280
At most 2 0.242971 14.53711 15.49471 0.0693
At most 3 0.001205 0.062691 3.841466 0.8023
84 Arthshodh
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.448062 30.90464 27.58434 0.0180
At most 1 0.284159 17.38343 21.13162 0.1547
At most 2 * 0.242971 14.47442 14.26460 0.0463
At most 3 0.001205 0.062691 3.841466 0.8023
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Source: Computed
The results highlight that there exists a long run relationship
among agricultural GDP and all the climate variables.
Results
Temperature has a negative impact on agricultural output,
revealing that if it goes by one degree then the agricultural output
will decline by Rs. 16220 crores. The coefficient value of rainfall
shows a positive impact, depicting that one additional milliliter (ml)
of rainfall will cause agricultural output to increase by Rs.972 crore.
Carbon emission has a negative influence on output from the
agricultural sector i.e. on an average an increase of one-unit emission
of carbon will cause agricultural output to decrease by Rs. 0.14 crore.
Impact of fertilizer consumption is significant in the model the
plausible reason for the insignificance of the consumption of
fertilizer could be its underestimated figures. The results highlight
that there exists a long run relationship among agricultural GDP and
all the climate variables. Thus, validating the theory that the impact
of climate change can only be visible on the long-run growth of the
agriculture.
Climate Change and Its Impact on Agricultural Production : An ........ 85
Conclusion
For majority of developing countries climate is an invisible
threat and if climate change response strategies were to be embraced
by these countries, it is imperative that such response strategies are
aligned to a development agenda. As according to Smit and Benhin
(2004), in order to mainstream climate change, it is important to pay
attention to climate change related issues that are presently
impacting the community, addressing the management or coping
strategies presently existing at local level and lastly revising the
policy structure that exists now to deal with these climatic issues i.e.
conducting impact assessment of the policies addressing the
measures of climate change adaptation and reformulating the policy
actions for more effective results to control the negative impact of
climate change. The results that all the regressors have expected
hypothesized sign as described in methodology. Impact of all the
climate change variables i.e. temperature, rainfall and carbon
emission are found to be significant. Temperature and Carbon
emission have negative impact on agricultural output and rainfall
shows a positive impact on output from the agricultural sector. The
impact of fertilizer consumption is significant in the model the
plausible reason for the insignificance of the consumption of
fertilizer could be its underestimated figures. The impact of climate
change is visible in long-run growth of agriculture thus, there is a
need to align climate change response strategies development
agenda. It is important to pay attention to climate change related
issues that are presently impacting the community, addressing the
management or coping strategies presently existing at local level
(Smith and Benhin (2004)). There is need to conducting impact
assessment of the policies addressing the measures of climate change
adaptation and reformulating the policy actions for more effective
results to control the negative impact of climate change.
86 Arthshodh
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90 Arthshodh
Appendix
Diagnostic tests for the Model
1) Multicollinearity
Variance Inflation Factors
Coefficient Uncentered Centered
Variable Variance VIF VIF
C 8608099. 1.006402 NA
D(ANNUAL_TEMPERAT
URE,2)
38002962 1.051612 1.051549
D(AVERAGE_RAINFALL,
2)
20668.64 1.287119 1.285981
D(CO2_EMISSION,2) 0.003824 1.133219 1.127241
D(CONSUMPTION_OF_F
ERTILISER,2)
9.454244 1.209599 1.209161
2) Autocorrelation
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 22.61523 Prob. F(2,46) 0.0000
Obs*R-squared 26.27647 Prob. Chi-Square(2) 0.0000
3) Heteroskedasticity
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 0.836313 Prob. F(4,48) 0.5089
Obs*R-squared 3.453064 Prob. Chi-Square(4) 0.4851
Scaled explained
SS
3.706266 Prob. Chi-Square(4) 0.4472
Climate Change and Its Impact on Agricultural Production : An ........ 91
4) Normality Test
0
2
4
6
8
10
12
14
-60000 -40000 -20000 0 20000 40000
Series: ResidualsSample 1962 2014Observations 53
Mean 1.17e-12Median -5399.618Maximum 50105.65Minimum -61615.95Std. Dev. 20456.19Skewness -0.014516Kurtosis 3.617166
Jarque-Bera 0.843001Probability 0.656062
Accounting Studies i Volume 11 No. 1 May, 2013
About Arthshodh
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